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Quantified Facial Temporal-Expressiveness Dynamics for Affect Analysis

Md Taufeeq Uddin, Shaun Canavan

Auto-TLDR; quantified facial Temporal-expressiveness Dynamics for quantified affect analysis

Abstract Poster

The quantification of visual affect data (e.g. face images) is essential to build and monitor automated affect modeling systems efficiently. Considering this, this work proposes quantified facial Temporal-expressiveness Dynamics (TED) to quantify the expressiveness of human faces. The proposed algorithm leverages multimodal facial features by incorporating static and dynamic information to enable accurate measurements of facial expressiveness. We show that TED can be used for high-level tasks such as summarization of unstructured visual data, expectation from and interpretation of automated affect recognition models. To evaluate the positive impact of using TED, a case study was conducted on spontaneous pain using the UNBC-McMaster spontaneous shoulder pain dataset. Experimental results show the efficacy of using TED for quantified affect analysis.

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Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics

Benjamin Szczapa, Mohammed Daoudi, Stefano Berretti, Pietro Pala, Zakia Hammal, Alberto Del Bimbo

Auto-TLDR; Automatic Pain Intensity Measurement from Facial Points Using Gram Matrices

Abstract Slides Poster Similar

We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. Curve fitting and temporal alignment were then used to smooth the extracted trajectories. A SVR regression model was then trained to encode the extracted trajectories into ten pain intensity scores consistent with the Visual Analogue Scale for pain intensity measurement. The proposed approach was evaluated using the UNBC McMaster Shoulder Pain Expression database and compared to the state of the art on the same data. Using both 5-folds cross-validation and leave-one-subject-out cross-validation, our results are competitive with respect to state of the art methods.

Automatic Annotation of Corpora for Emotion Recognition through Facial Expressions Analysis

Alex Mircoli, Claudia Diamantini, Domenico Potena, Emanuele Storti

Auto-TLDR; Automatic annotation of video subtitles on the basis of facial expressions using machine learning algorithms

Abstract Slides Poster Similar

The recent diffusion of social networks has made available an unprecedented amount of user-generated content, which may be analyzed in order to determine people's opinions and emotions about a large variety of topics. Research has made many efforts in defining accurate algorithms for analyzing emotions expressed by users in texts; however, their performance often rely on the existence of large annotated datasets, whose current scarcity represents a major issue. The manual creation of such datasets represents a costly and time-consuming activity and hence there is an increasing demand for techniques for the automatic annotation of corpora. In this work we present a methodology for the automatic annotation of video subtitles on the basis of the analysis of facial expressions of people in videos, with the goal of creating annotated corpora that may be used to train emotion recognition algorithms. Facial expressions are analyzed through machine learning algorithms, on the basis of a set of manually-engineered facial features that are extracted from video frames. The soundness of the proposed methodology has been evaluated through an extensive experimentation aimed at determining the performance on real datasets of each methodological step.

Attribute-Based Quality Assessment for Demographic Estimation in Face Videos

Fabiola Becerra-Riera, Annette Morales-González, Heydi Mendez-Vazquez, Jean-Luc Dugelay

Auto-TLDR; Facial Demographic Estimation in Video Scenarios Using Quality Assessment

Abstract Slides Similar

Most existing works regarding facial demographic estimation are focused on still image datasets, although nowadays the need to analyze video content in real applications is increasing. We propose to tackle gender, age and ethnicity estimation in the context of video scenarios. Our main contribution is to use an attribute-specific quality assessment procedure to select best quality frames from a video sequence for each of the three demographic modalities. Best quality frames are classified with fine-tuned MobileNet models and a final video prediction is obtained with a majority voting strategy among the best selected frames. Our validation on three different datasets and our comparison with state-of-the-art models, show the effectiveness of the proposed demographic classifiers and the quality pipeline, which allows to reduce both: the number of frames to be classified and the processing time in practical applications; and improves the soft biometrics prediction accuracy.

Interpretable Emotion Classification Using Temporal Convolutional Models

Manasi Bharat Gund, Abhiram Ravi Bharadwaj, Ifeoma Nwogu

Auto-TLDR; Understanding the Dynamics of Facial Emotion Expression with Spatiotemporal Representations

Abstract Slides Poster Similar

As with many problems solved by deep neural networks, existing solutions rarely explain, precisely, the important factors responsible for the predictions made by the model. This work looks to investigate how different spatial regions and landmark points change in position over time, to better explain the underlying factors responsible for various facial emotion expressions. By pinpointing the specific regions or points responsible for the classification of a particular facial expression, we gain better insight into the dynamics of the face when displaying that emotion. To accomplish this, we examine two spatiotemporal representations of moving faces, while expressing different emotions. The representations are then presented to a convolutional neural network for emotion classification. Class activation maps are used in highlighting the regions of interest and the results are qualitatively compared with the well known facial action units, using the facial action coding system. The model was originally trained and tested on the CK+ dataset for emotion classification, and then generalized to the SAMM dataset. In so doing, we successfully present an interpretable technique for understanding the dynamics that occur during convolutional-based prediction tasks on sequences of face data.

Self-Supervised Learning of Dynamic Representations for Static Images

Siyang Song, Enrique Sanchez, Linlin Shen, Michel Valstar

Auto-TLDR; Facial Action Unit Intensity Estimation and Affect Estimation from Still Images with Multiple Temporal Scale

Abstract Slides Poster Similar

Facial actions are spatio-temporal signals by nature, and therefore their modeling is crucially dependent on the availability of temporal information. In this paper, we focus on inferring such temporal dynamics of facial actions when no explicit temporal information is available, i.e. from still images. We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation. In particular, 1) we propose a framework that infers a dynamic representation (DR) from a still image, which captures the bi-directional flow of time within a short time-window centered at the input image; 2) we show that we can train our method without the need of explicitly generating target representations, allowing the network to represent dynamics more broadly; and 3) we propose to apply a multiple temporal scale approach that infers DRs for different window lengths (MDR) from a still image. We empirically validate the value of our approach on the task of frame ranking, and show how our proposed MDR attains state of the art results on BP4D for AU intensity estimation and on SEMAINE for dimensional affect estimation, using only still images at test time.

Responsive Social Smile: A Machine-Learning Based Multimodal Behavior Assessment Framework towards Early Stage Autism Screening

Yueran Pan, Kunjing Cai, Ming Cheng, Xiaobing Zou, Ming Li

Auto-TLDR; Responsive Social Smile: A Machine Learningbased Assessment Framework for Early ASD Screening

Abstract Poster Similar

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which causes social deﬁcits in social lives. Early ASD screening for children is an important method to reduce the impact of ASD on people’s whole lives. Traditional screening methods rely on protocol experiments and subjective evaluations from clinicians and domain experts and thereby cost a lot. To standardize the process of ASD screening, we 1 collaborate with a group of ASD experts, and design a ”Responsive Social Smile” protocol and an experiment environment. Also, we propose a machine learningbased assessment framework for early ASD screening. By integrating technologies of speech recognition and computer vision, the framework can quantitatively analyze the behaviors of children under well-designed protocols. By collecting 196 test samples from 41 children in the clinical treatments, our proposed method obtains 85.20% accuracy for the score prediction of individual protocol, and 80.49% unweighted accuracy for the ﬁnal ASD prediction. This result indicates that our model reaches the average level of domain experts in ASD diagnosis.

Depth Videos for the Classification of Micro-Expressions

Ankith Jain Rakesh Kumar, Bir Bhanu, Christopher Casey, Sierra Cheung, Aaron Seitz

Auto-TLDR; RGB-D Dataset for the Classification of Facial Micro-expressions

Abstract Slides Poster Similar

Facial micro-expressions are spontaneous, subtle, involuntary muscle movements occurring briefly on the face. The spotting and recognition of these expressions are difficult due to the subtle behavior, and the time duration of these expressions is about half a second, which makes it difficult for humans to identify them. These micro-expressions have many applications in our daily life, such as in the field of online learning, game playing, lie detection, and therapy sessions. Traditionally, researchers use RGB images/videos to spot and classify these micro-expressions, which pose challenging problems, such as illumination, privacy concerns and pose variation. The use of depth videos solves these issues to some extent, as the depth videos are not susceptible to the variation in illumination. This paper describes the collection of a first RGB-D dataset for the classification of facial micro-expressions into 6 universal expressions: Anger, Happy, Sad, Fear, Disgust, and Surprise. This paper shows the comparison between the RGB and Depth videos for the classification of facial micro-expressions. Further, a comparison of results shows that depth videos alone can be used to classify facial micro-expressions correctly in a decision tree structure by using the traditional and deep learning approaches with good classification accuracy. The dataset will be released to the public in the near future.

Learning Emotional Blinded Face Representations

Alejandro Peña Almansa, Julian Fierrez, Agata Lapedriza, Aythami Morales

Auto-TLDR; Blind Face Representations for Emotion Recognition

Abstract Slides Poster Similar

This work proposes two new face representations that are blind to the expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which force data controllers to protect any kind of sensitive information involved in automatic processes. The advances in affective computing have contributed to improve human-machine interfaces, but at the same time, the capacity to monitorize emotional responses trigger potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these facial expression blinded features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers over a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks.

Pose-Based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation

Zhengyuan Yang, Amanda Kay, Yuncheng Li, Wendi Cross, Jiebo Luo

Auto-TLDR; Body Language Based Emotion Recognition for Psychiatric Symptoms Prediction

Abstract Slides Poster Similar

Inspired by the human ability to infer emotions from body language, we propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we further extend the framework for psychiatric symptom prediction. Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set and possess a good transferability. The proposed system in the first stage generates sequences of body language predictions based on human poses estimated from input videos. In the second stage, the predicted sequences are fed into a temporal network for emotion interpretation and psychiatric symptom prediction. We first validate the accuracy and transferability of the proposed body language recognition method on several public action recognition datasets. We then evaluate the framework on a proposed URMC dataset, which consists of conversations between a standardized patient and a behavioral health professional, along with expert annotations of body language, emotions, and potential psychiatric symptoms. The proposed framework outperforms other methods on the URMC dataset.

Siamese-Structure Deep Neural Network Recognizing Changes in Facial Expression According to the Degree of Smiling

Kazuaki Kondo, Taichi Nakamura, Yuichi Nakamura, Shin'Ichi Satoh

Auto-TLDR; A Siamese-Structure Deep Neural Network for Happiness Recognition

Abstract Slides Poster Similar

A smile is a representative expression of happiness or high quality-of-life; however, automatic recognition of a smile according to happiness remains a challenging task. Because expressions of happiness are strongly dependent upon physical condition and occurrence of other emotions, and similar facial expression often occur under different emotions, we consider that there is no absolute visual pattern of a smile corresponding to happiness. Therefore, in this study, we assumed that a ``smile with happiness'' is observed as the temporal ascent in the degree of smiling and attempted to recognize this by capturing changes in facial expression within temporally sequential images. As an implementation of this scheme, we proposed a Siamese-structure deep neural network to compare facial expressions in two input images and estimate the existence of smile ascension or descension. For primal analysis of the proposed network, we developed a unique smiling dataset containing image pairs with various changes in smiling degree, including slight changes. The results demonstrated that the proposed method achieved nearly perfect recognition with >0.95 accuracy when recognizing changes in the degree of smiling that humans certainly recognize. Attention regions that contributed to the predicted labels were concentrated on the mouth, cheeks, and tail of the eyes, which indicates a reasonable function for recognizing changes in smiling degree was constructed by the proposed method.

Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

Nina Weng, Jiahao Wang, Annan Li, Yunhong Wang

Auto-TLDR; 2S-TCN: A Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

Abstract Slides Poster Similar

In the field of facial attractiveness prediction, while deep models using static pictures have shown promising results, little attention is paid to dynamic facial information, which is proven to be influential by psychological studies. Meanwhile, the increasing popularity of short video apps creates an enormous demand of facial attractiveness prediction from short video clips. In this paper, we target on the dynamic facial attractiveness prediction problem. To begin with, a large-scale video-based facial attractiveness prediction dataset (VFAP) with more than one thousand clips from TikTok is collected. A two-stream temporal convolutional network (2S-TCN) is then proposed to capture dynamic attractiveness feature from both facial appearance and landmarks. We employ attentive feature enhancement along with specially designed modality and temporal fusion strategies to better explore the temporal dynamics. Extensive experiments on the proposed VFAP dataset demonstrate that 2S-TCN has a distinct advantage over the state-of-the-art static prediction methods.

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Zhihua Li, Zheng Zhang, Lijun Yin

Auto-TLDR; Temporal Fusion and Self-Attention Network for Facial Action Unit Detection

Abstract Slides Poster Similar

Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so-called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

MRP-Net: A Light Multiple Region Perception Neural Network for Multi-Label AU Detection

Yang Tang, Shuang Chen, Honggang Zhang, Gang Wang, Rui Yang

Auto-TLDR; MRP-Net: A Fast and Light Neural Network for Facial Action Unit Detection

Abstract Slides Poster Similar

Facial Action Units (AUs) are of great significance in communication. Automatic AU detection can improve the understanding of psychological condition and emotional status. Recently, a number of deep learning methods have been proposed to take charge with problems in automatic AU detection. Several challenges, like unbalanced labels and ignorance of local information, remain to be addressed. In this paper, we propose a fast and light neural network called MRP-Net, which is an end-to-end trainable method for facial AU detection to solve these problems. First, we design a Multiple Region Perception (MRP) module aimed at capturing different locations and sizes of features in the deeper level of the network without facial landmark points. Then, in order to balance the positive and negative samples in the large dataset, a batch balanced method adjusting the weight of every sample in one batch in our loss function is suggested. Experimental results on two popular AU datasets, BP4D and DISFA prove that MRP-Net outperforms state-of-the-art methods. Compared with the best method, not only does MRP-Net have an average F1 score improvement of 2.95% on BP4D and 5.43% on DISFA, and it also decreases the number of network parameters by 54.62% and the number of network FLOPs by 19.6%.

Facial Expression Recognition Using Residual Masking Network

Luan Pham, Vu Huynh, Tuan Anh Tran

Auto-TLDR; Deep Residual Masking for Automatic Facial Expression Recognition

Abstract Slides Poster Similar

Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets. Our works are available on Github.

Identity-Aware Facial Expression Recognition in Compressed Video

Xiaofeng Liu, Linghao Jin, Xu Han, Jun Lu, Jonghye Woo, Jane You

Auto-TLDR; Exploring Facial Expression Representation in Compressed Video with Mutual Information Minimization

Abstract Slides Similar

This paper targets to explore the inter-subject variations eliminated facial expression representation in the compressed video domain. Most of the previous methods process the RGB images of a sequence, while the off-the-shelf and valuable expression-related muscle movement already embedded in the compression format. In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network. By enforcing the marginal independent of them, the expression feature is expected to be purer for the expression and be robust to identity shifts. Specifically, we propose a novel collaborative min-min game for mutual information (MI) minimization in latent space. We do not need the identity label or multiple expression samples from the same person for identity elimination. Moreover, when the apex frame is annotated in the dataset, the complementary constraint can be further added to regularize the feature-level game. In testing, only the compressed residual frames are required to achieve expression prediction. Our solution can achieve comparable or better performance than the recent decoded image based methods on the typical FER benchmarks with about 3$\times$ faster inference with compressed data.

Learning Visual Voice Activity Detection with an Automatically Annotated Dataset

Stéphane Lathuiliere, Pablo Mesejo, Radu Horaud

Auto-TLDR; Deep Visual Voice Activity Detection with Optical Flow

Abstract Slides Similar

Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. V-VAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing V-VAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets in-the-wild, based on combining A-VAD and face detection. A thorough empirical evaluation shows the advantage of training the proposed deep V-VAD models with such a dataset.

Video-Based Facial Expression Recognition Using Graph Convolutional Networks

Daizong Liu, Hongting Zhang, Pan Zhou

Auto-TLDR; Graph Convolutional Network for Video-based Facial Expression Recognition

Abstract Slides Poster Similar

Facial expression recognition (FER), aiming to classify the expression present in the facial image or video, has attracted a lot of research interests in the field of artificial intelligence and multimedia. In terms of video based FER task, it is sensible to capture the dynamic expression variation among the frames to recognize facial expression. However, existing methods directly utilize CNN-RNN or 3D CNN to extract the spatial-temporal features from different facial units, instead of concentrating on a certain region during expression variation capturing, which leads to limited performance in FER. In our paper, we introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based FER. First, the GCN layer is utilized to learn more contributing facial expression features which concentrate on certain regions after sharing information between nodes those represent CNN extracted features. Then, a LSTM layer is applied to learn long-term dependencies among the GCN learned features to model the variation. In addition, a weight assignment mechanism is also designed to weight the output of different nodes for final classification by characterizing the expression intensities in each frame. To the best of our knowledge, it is the first time to use GCN in FER task. We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0, and the experimental results demonstrate that our method has superior performance to existing methods.

Sequential Non-Rigid Factorisation for Head Pose Estimation

Stefania Cristina, Kenneth Patrick Camilleri

Auto-TLDR; Sequential Shape-and-Motion Factorisation for Head Pose Estimation in Eye-Gaze Tracking

Abstract Slides Poster Similar

Within the context of eye-gaze tracking, the capability of permitting the user to move naturally is an important step towards allowing for more natural user interaction in less constrained scenarios. Natural movement can be characterised by changes in head pose, as well as non-rigid face deformations as the user performs different facial expressions. While the estimation of head pose within the domain of eye-gaze tracking is being increasingly considered, the face is most often regarded as a rigid body. The few methods that factor the challenge of handling face deformations into the head pose estimation problem, often require the availability of a pre-defined face model or a considerable amount of training data. In this paper, we direct our attention towards the application of shape-and-motion factorisation for head pose estimation, since this does not generally rely on the availability of an initial face model. Over the years, various shape-and-motion factorisation methods have been proposed to address the challenges of rigid and non-rigid shape and motion recovery, in a batch or sequential manner. However, the real-time recovery of non-rigid shape and motion by factorisation remains, in general, an open problem. Our work addresses this open problem by proposing a sequential factorisation method for non-rigid shape and motion recovery, which does not rely on the availability of a pre-defined face deformation model or training data. Quantitative and qualitative results show that our method can handle various non-rigid face deformations without deterioration of the head pose estimation accuracy.

Audio-Video Detection of the Active Speaker in Meetings

Francisco Madrigal, Frederic Lerasle, Lionel Pibre, Isabelle Ferrané

Auto-TLDR; Active Speaker Detection with Visual and Contextual Information from Meeting Context

Abstract Slides Poster Similar

Meetings are a common activity that provides certain challenges when creating systems that assist them. Such is the case of the Active speaker detection, which can provide useful information for human interaction modeling, or human-robot interaction. Active speaker detection is mostly done using speech, however, certain visual and contextual information can provide additional insights. In this paper we propose an active speaker detection framework that integrates audiovisual features with social information, from the meeting context. Visual cue is processed using a Convolutional Neural Network (CNN) that captures the spatio-temporal relationships. We analyze several CNN architectures with both cues: raw pixels (RGB images) and motion (estimated with optical flow). Contextual reasoning is done with an original methodology, based on the gaze of all participants. We evaluate our proposal with a public \textcolor{black}{benchmark} in state-of-art: AMI corpus. We show how the addition of visual and context information improves the performance of the active speaker detection.

Real-Time Driver Drowsiness Detection Using Facial Action Units

Malaika Vijay, Nandagopal Netrakanti Vinayak, Maanvi Nunna, Subramanyam Natarajan

Auto-TLDR; Real-Time Detection of Driver Drowsiness using Facial Action Units using Extreme Gradient Boosting

Abstract Slides Poster Similar

This paper presents a two-stage, vision-based pipeline for the real-time detection of driver drowsiness using Facial Action Units (FAUs). FAUs capture movements in groups of muscles in the face like widening of the eyes or dropping of the jaw. The first stage of the pipeline employs a Convolutional Neural Network (CNN) trained to detect FAUs. The output of the penultimate layer of this network serves as an image embedding that captures features relevant to FAU detection. These embeddings are then used to predict drowsiness using an Extreme Gradient Boosting (XGBoost) classifier. A separate XGBoost model is trained for each user of the system so that behavior specific to each user can be modeled into the drowsiness classifier. We show that user-specific classifiers require very little data and low training time to yield high prediction accuracies in real-time.

Spatial Bias in Vision-Based Voice Activity Detection

Kalin Stefanov, Mohammad Adiban, Giampiero Salvi

Auto-TLDR; Spatial Bias in Vision-based Voice Activity Detection in Multiparty Human-Human Interactions

Abstract Poster Similar

We present models for automatic vision-based voice activity detection (VAD) in multiparty human-human interactions that are aimed at complementing the acoustic VAD methods. We provide evidence that this type of vision-based VAD models are susceptible to spatial bias in the datasets. The physical settings of the interaction, usually constant throughout data acquisition, determines the distribution of head poses of the participants. Our results show that when the head pose distributions are significantly different in the training and test sets, the performance of the models drops significantly. This suggests that previously reported results on datasets with a fixed physical configuration may overestimate the generalization capabilities of this type of models. We also propose a number of possible remedies to the spatial bias, including data augmentation, input masking and dynamic features, and provide an in-depth analysis of the visual cues used by our models.

A Quantitative Evaluation Framework of Video De-Identification Methods

Sathya Bursic, Alessandro D'Amelio, Marco Granato, Giuliano Grossi, Raffaella Lanzarotti

Auto-TLDR; Face de-identification using photo-reality and facial expressions

Abstract Slides Poster Similar

We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted methods to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The method is applied to four open-source tools, producing a baseline for future de-identification methods.

Inner Eye Canthus Localization for Human Body Temperature Screening

Claudio Ferrari, Lorenzo Berlincioni, Marco Bertini, Alberto Del Bimbo

Auto-TLDR; Automatic Localization of the Inner Eye Canthus in Thermal Face Images using 3D Morphable Face Model

Abstract Slides Poster Similar

In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data will be publicly released.

Classifying Eye-Tracking Data Using Saliency Maps

Shafin Rahman, Sejuti Rahman, Omar Shahid, Md. Tahmeed Abdullah, Jubair Ahmed Sourov

Auto-TLDR; Saliency-based Feature Extraction for Automatic Classification of Eye-tracking Data

Abstract Slides Poster Similar

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-fixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD [1], Age Prediction [2], and Visual Perceptual Task [3] dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods [2],[4], [5] by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.

Detecting Anomalies from Video-Sequences: A Novel Descriptor

Giulia Orrù, Davide Ghiani, Maura Pintor, Gian Luca Marcialis, Fabio Roli

Auto-TLDR; Trit-based Measurement of Group Dynamics for Crowd Behavior Analysis and Anomaly Detection

Abstract Slides Poster Similar

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed in a time window. An appropriate measurement unit, named "trit" (trinary digit), represents three possible dynamic states of groups on a certain frame. Our hypothesis is that abrupt variations of the groups' number may be due to an anomalous event that can be accordingly detected, by translating these variations on temporal trit-based sequence of strings which are significantly different from the one describing the "no-anomaly" one. Due to the peculiarity of the rationale behind this work, relying on the number of groups, three different methods of people group's extraction are compared. Experiments are carried out on the Motion-Emotion benchmark data set. Reported results point out in which cases the trit-based measurement of group dynamics allows us to detect the anomaly. Besides the promising performance of our approach, we show how it is correlated with the anomaly typology and the camera's perspective to the crowd's flow (frontal, lateral).

A General End-To-End Method for Characterizing Neuropsychiatric Disorders Using Free-Viewing Visual Scanning Tasks

Hong Yue Sean Liu, Jonathan Chung, Moshe Eizenman

Auto-TLDR; A general, data-driven, end-to-end framework that extracts relevant features of attentional bias from visual scanning behaviour and uses these features

Abstract Slides Poster Similar

The growing availability of eye-gaze tracking technology has allowed for its employment in a wide variety of applications, one of which is the objective diagnosis and monitoring of neuropsychiatric disorders from features of attentional bias extracted from visual scanning patterns. Current techniques in this field are largely comprised of non-generalizable methodologies that rely on domain expertise and study-specific assumptions. In this paper, we present a general, data-driven, end-to-end framework that extracts relevant features of attentional bias from visual scanning behaviour and uses these features to classify between subject groups with standard machine learning techniques. During the free-viewing task, subjects view sets of slides with thematic images while their visual scanning patterns (sets of ordered fixations) are monitored by an eye-tracking system. We encode fixations into relative visual attention maps (RVAMs) to describe measurement errors, and two data-driven methods are proposed to segment regions of interests from RVAMs: 1) using group average RVAMs, and 2) using difference of group average RVAMs. Relative fixation times within regions of interest are calculated and used as input features for a vanilla multilayered perceptron to classify between patient groups. The methods were evaluated on data from an anorexia nervosa (AN) study with 37 subjects and a bipolar/major depressive disorder (BD-MDD) study with 73 subjects. Using leave-one-subject-out cross validation, our technique is able to achieve an area under the receiver operating curve (AUROC) score of 0.935 for the AN study and 0.888 for the BD-MDD study, the latter of which exceeds the performance of the state-of-the-art analysis model designed specifically for the BD-MDD study, which had an AUROC of 0.879. The results validate the proposed methods' efficacy as generalizable, standard baselines for analyzing visual scanning data.

Magnifying Spontaneous Facial Micro Expressions for Improved Recognition

Pratikshya Sharma, Sonya Coleman, Pratheepan Yogarajah, Laurence Taggart, Pradeepa Samarasinghe

Auto-TLDR; Eulerian Video Magnification for Micro Expression Recognition

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Building an effective automatic micro expression recognition (MER) system is becoming increasingly desirable in computer vision applications. However, it is also very challenging given the fine-grained nature of the expressions to be recognized. Hence, we investigate if amplifying micro facial muscle movements as a pre-processing phase, by employing Eulerian Video Magnification (EVM), can boost performance of Local Phase Quantization with Three Orthogonal Planes (LPQ-TOP) to achieve improved facial MER across various datasets. In addition, we examine the rate of increase for recognition to determine if it is uniform across datasets using EVM. Ultimately, we classify the extracted features using Support Vector Machines (SVM). We evaluate and compare the performance with various methods on seven different datasets namely CASME, CAS(ME)2, CASME2, SMIC-HS, SMIC-VIS, SMIC-NIR and SAMM. The results obtained demonstrate that EVM can enhance LPQ-TOP to achieve improved recognition accuracy on the majority of the datasets.

Deep Multi-Task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing

Rui Zhao, Tianshan Liu, Jun Xiao, P. K. Daniel Lun, Kin-Man Lam

Auto-TLDR; Multi-task Learning for Facial Expression Recognition and Synthesis

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Multi-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration the feature selection, when transferring information between different tasks, which may lead to task interference when training the multi-task networks. To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis. The proposed method can effectively transfer beneficial features between different tasks, while filtering out useless and harmful information. Moreover, we employ the facial expression synthesis task to enlarge and balance the training dataset to further enhance the generalization ability of the proposed method. Experimental results show that the proposed method achieves state-of-the-art performance on those commonly used facial expression recognition benchmarks, which makes it a potential solution to real-world facial expression recognition problems.

Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos

Elahe Vahdani, Longlong Jing, Ying-Li Tian, Matt Huenerfauth

Auto-TLDR; ASL-HW-RGBD: Recognizing Grammatical Errors in Continuous Sign Language

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As part of the development of an educational tool that can help students achieve fluency in American Sign Language (ASL) through independent and interactive practice with immediate feedback, this paper introduces a near real-time system to recognize grammatical errors in continuous signing videos without necessarily identifying the entire sequence of signs. Our system automatically recognizes if a performance of ASL sentences contains grammatical errors made by ASL students. We first recognize the ASL grammatical elements including both manual gestures and nonmanual signals independently from multiple modalities (i.e. hand gestures, facial expressions, and head movements) by 3D-ResNet networks. Then the temporal boundaries of grammatical elements from different modalities are examined to detect ASL grammatical mistakes by using a sliding window-based approach. We have collected a dataset of continuous sign language, ASL-HW-RGBD, covering different aspects of ASL grammars for training and testing. Our system is able to recognize grammatical elements on ASL-HW-RGBD from manual gestures, facial expressions, and head movements and successfully detect 8 ASL grammatical mistakes.

AttendAffectNet: Self-Attention Based Networks for Predicting Affective Responses from Movies

Thi Phuong Thao Ha, Bt Balamurali, Herremans Dorien, Roig Gemma

Auto-TLDR; AttendAffectNet: A Self-Attention Based Network for Emotion Prediction from Movies

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In this work, we propose different variants of the self-attention based network for emotion prediction from movies, which we call AttendAffectNet. We take both audio and video into account and incorporate the relation among multiple modalities by applying self-attention mechanism in a novel manner into the extracted features for emotion prediction. We compare it to the typically temporal integration of the self-attention based model, which in our case, allows to capture the relation of temporal representations of the movie while considering the sequential dependencies of emotion responses. We demonstrate the effectiveness of our proposed architectures on the extended COGNIMUSE dataset [1], [2] and the MediaEval 2016 Emotional Impact of Movies Task [3], which consist of movies with emotion annotations. Our results show that applying the self-attention mechanism on the different audio-visual features, rather than in the time domain, is more effective for emotion prediction. Our approach is also proven to outperform state-of-the-art models for emotion prediction.

Using Meta Labels for the Training of Weighting Models in a Sample-Specific Late Fusion Classification Architecture

Peter Bellmann, Patrick Thiam, Friedhelm Schwenker

Auto-TLDR; A Late Fusion Architecture for Multiple Classifier Systems

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The performance of multiple classifier systems can be significantly improved by the use of intelligent classifier combination approaches. In this study, we introduce a novel late fusion architecture, which can be interpreted as a combination of the well-known mixture of experts and stacked generalization methods. Our proposed method aggregates the outputs of classification models and corresponding sample-specific weighting models. A special feature of our proposed architecture is that each weighting model is trained on an individual set of meta labels. Using individual sets of meta labels allows each weighting model to separate regions, on which the predictions of the corresponding classification model can be associated to an estimated confidence value. We test our proposed architecture on a set of publicly available databases, including different benchmark data sets. The experimental evaluation shows the effectiveness and potential of our proposed method. Moreover, we discuss different approaches for further improvement of our proposed architecture.

Electroencephalography Signal Processing Based on Textural Features for Monitoring the Driver’s State by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis

Auto-TLDR; One-dimensional Local Binary Pattern Algorithm for Estimating Driver Vigilance in a Brain-Computer Interface System

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In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily "close" as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.

Estimation of Clinical Tremor Using Spatio-Temporal Adversarial AutoEncoder

Li Zhang, Vidya Koesmahargyo, Isaac Galatzer-Levy

Auto-TLDR; ST-AAE: Spatio-temporal Adversarial Autoencoder for Clinical Assessment of Hand Tremor Frequency and Severity

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Collecting sufficient well-labeled training data is a challenging task in many clinical applications. Besides the tremendous efforts required for data collection, clinical assessments are also impacted by raters’ variabilities, which may be significant even among experienced clinicians. The high demands of reproducible and scalable data-driven approaches in these areas necessitates relevant research on learning with limited data. In this work, we propose a spatio-temporal adversarial autoencoder (ST-AAE) for clinical assessment of hand tremor frequency and severity. The ST-AAE integrates spatial and temporal information simultaneously into the original AAE, taking optical flows as inputs. Using only optical flows, irrelevant background or static objects from RGB frames are largely eliminated, so that the AAE is directed to effectively learn key feature representations of the latent space from tremor movements. The ST-AAE was evaluated with both volunteer and clinical data. The volunteer results showed that the ST-AAE improved model performance significantly by 15% increase on accuracy. Leave-one-out (on subjects) cross validation was used to evaluate the accuracy for all the 3068 video segments from 28 volunteers. The weighted average of the AUCs of ROCs is 0.97. The results demonstrated that the ST-AAE model, trained with a small number of subjects, can be generalized well to different subjects. In addition, the model trained only by volunteer data was also evaluated with 32 clinical videos from 9 essential tremor patients, the model predictions correlate well with the clinical ratings: correlation coefficient r = 0.91 and 0.98 for in-person ratings and video watching ratings, respectively.

Assessing the Severity of Health States Based on Social Media Posts

Shweta Yadav, Joy Prakash Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

Auto-TLDR; A Multiview Learning Framework for Assessment of Health State in Online Health Communities

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The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients' social media posts can help health professionals (HP) in prioritizing the user’s post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user’s health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user’s health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user’s health.

Unconstrained Facial Expression Recogniton Based on Cascade Decision and Gabor Filters

Yanhong Wu, Lijie Zhang, Guannan Chen, Pablo Navarrete Michelini

Auto-TLDR; Convolutional Neural Network for Facial Expression Recognition under unconstrained natural conditions

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Facial Expression Recognition (FER) research with Convolutional Neural Networks (CNN) has been active, especially under unconstrained natural conditions. From our observation, prior arts treat expressions equally in classification and the reconition accuracy of some expression are always higher than others. In this paper, we make the assumption that an expression with a higher accuracy is easier to be recognized, and those expressions easier to recognize will hinder the recognition of uneasy expressions. Then, we propose a novel algorithm for unconstrained FER based on cascade decision and Gabor filters. Easier expressions are recognized before the difficult expressions. This simple method trains up to five models to cascadedly recognize a given facial image expression. The first binary classifier model is for the classification of Happy with the highest accuracy. The second binary classifier model is for the classification of Surprise with the second high accuracy. The third binary classifier model is for the classification of Neutral with the third high accuracy. The forth model is for the classification of Sad with the forth high accuracy. And the final model is 3-class classifier for Angry, Disgust and Fear. Gabor filters are included in every model to enhance robustness on illumination variations and face poses. Extensive experiment results on several datasets validate the effectiveness of the proposed method. We obtain accuracy of 77.6% on FER2013 with the final models, outperforming the latest state-of-the-arts.

Exposing Deepfake Videos by Tracking Eye Movements

Meng Li, Beibei Liu, Yujiang Hu, Yufei Wang

Auto-TLDR; A Novel Approach to Detecting Deepfake Videos

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It has recently become a major threat to the public media that fake videos are rapidly spreading over the Internet. The advent of Deepfake, a deep-learning based toolkit, has facilitated a massive abuse of improper synthesized videos, which may influence the media credibility and human rights. A worldwide alert has been set off that finding ways to detect such fake videos is not only crucial but also urgent. This paper reports a novel approach to expose deepfake videos. We found that most fake videos are markedly different from the real ones in the way the eyes move. We are thus motivated to define four features that could well capture such differences. The features are then fed to SVM for classification. It is shown to be a promising approach that without high dimensional features and complicated neural networks, we are able to achieve competitive results on several public datasets. Moreover, the proposed features could well participate with other existing methods in the confrontation with deepfakes.

Learning Dictionaries of Kinematic Primitives for Action Classification

Alessia Vignolo, Nicoletta Noceti, Alessandra Sciutti, Francesca Odone, Giulio Sandini

Auto-TLDR; Action Understanding using Visual Motion Primitives

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This paper proposes a method based on visual motion primitives to address the problem of action understanding. The approach builds in an unsupervised way a dictionary of kinematic primitives from a set of sub-movements obtained by segmenting the velocity profile of an action on the basis of local minima derived directly from the optical flow. The dictionary is then used to describe each sub-movement as a linear combination of atoms using sparse coding. The descriptive capability of the proposed motion representation is experimentally validated on the MoCA dataset, a collection of synchronized multi-view videos and motion capture data of cooking activities. The results show that the approach, despite its simplicity, has a good performance in action classification, especially when the motion primitives are combined over time. Also, the method is proved to be tolerant to view point changes, and can thus support cross-view action recognition. Overall, the method may be seen as a backbone of a general approach to action understanding, with potential applications in robotics.

End-To-End Triplet Loss Based Emotion Embedding System for Speech Emotion Recognition

Puneet Kumar, Sidharth Jain, Balasubramanian Raman, Partha Pratim Roy, Masakazu Iwamura

Auto-TLDR; End-to-End Neural Embedding System for Speech Emotion Recognition

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In this paper, an end-to-end neural embedding system based on triplet loss and residual learning has been proposed for speech emotion recognition. The proposed system learns the embeddings from the emotional information of the speech utterances. The learned embeddings are used to recognize the emotions portrayed by given speech samples of various lengths. The proposed system implements Residual Neural Network architecture. It is trained using softmax pre-training and triplet loss function. The weights between the fully connected and embedding layers of the trained network are used to calculate the embedding values. The embedding representations of various emotions are mapped onto a hyperplane, and the angles among them are computed using the cosine similarity. These angles are utilized to classify a new speech sample into its appropriate emotion class. The proposed system has demonstrated 91.67\% and 64.44\% accuracy while recognizing emotions for RAVDESS and IEMOCAP dataset, respectively.

Modeling Long-Term Interactions to Enhance Action Recognition

Alejandro Cartas, Petia Radeva, Mariella Dimiccoli

Auto-TLDR; A Hierarchical Long Short-Term Memory Network for Action Recognition in Egocentric Videos

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In this paper, we propose a new approach to understand actions in egocentric videos that exploit the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical Long Short-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks, without relying on motion information.

Quality-Based Representation for Unconstrained Face Recognition

Nelson Méndez-Llanes, Katy Castillo-Rosado, Heydi Mendez-Vazquez, Massimo Tistarelli

Auto-TLDR; activation map for face recognition in unconstrained environments

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Significant advances have been achieved in face recognition in the last decade thanks to the development of deep learning methods. However, recognizing faces captured in uncontrolled environments is still a challenging problem for the scientific community. In these scenarios, the performance of most of existing deep learning based methods abruptly falls, due to the bad quality of the face images. In this work, we propose to use an activation map to represent the quality information in a face image. Different face regions are analyzed to determine their quality and then only those regions with good quality are used to perform the recognition using a given deep face model. For experimental evaluation, in order to simulate unconstrained environments, three challenging databases, with different variations in appearance, were selected: the Labeled Faces in the Wild Database, the Celebrities in Frontal-Profile in the Wild Database, and the AR Database. Three deep face models were used to evaluate the proposal on these databases and in all cases, the use of the proposed activation map allows the improvement of the recognition rates obtained by the original models in a range from 0.3 up to 31%. The obtained results experimentally demonstrated that the proposal is able to select those face areas with higher discriminative power and enough identifying information, while ignores the ones with spurious information.

Estimating Gaze Points from Facial Landmarks by a Remote Spherical Camera

Shigang Li

Auto-TLDR; Gaze Point Estimation from a Spherical Image from Facial Landmarks

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From a spherical image, a gaze point, instead of gaze vectors, can be estimated directly because a remote spherical camera can observe a user's face and a gaze target simultaneously. This paper investigates the problem of estimating a gaze point in a spherical image from facial landmarks. In contrast with the existing methods which usually assume gaze points move on a narrow plane, the proposed method can cope with the situation where gaze points vary in depth for a relatively wide field of view. As shown in the results of comparative experiments, we find the orthogonal coordinates of facial landmarks on a unit sphere is a reasonable representation in comparison with spherical polar coordinates; the cues of head pose is helpful to improve the accuracy of gaze points. Consequently, the proposed method achieves a performance on the accuracy of gaze points estimation which is comparable to the state of the art methods.

Video Analytics Gait Trend Measurement for Fall Prevention and Health Monitoring

Lawrence O'Gorman, Xinyi Liu, Md Imran Sarker, Mariofanna Milanova

Auto-TLDR; Towards Health Monitoring of Gait with Deep Learning

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We design a video analytics system to measure gait over time and detect trend and outliers in the data. The purpose is for health monitoring, the thesis being that trend especially can lead to early detection of declining health and be used to prevent accidents such as falls in the elderly. We use the OpenPose deep learning tool for recognizing the back and neck angle features of walking people, and measure speed as well. Trend and outlier statistics are calculated upon time series of these features. A challenge in this work is lack of testing data of decaying gait. We first designed experiments to measure consistency of the system on a healthy population, then analytically altered this real data to simulate gait decay. Results on about 4000 gait samples of 50 people over 3 months showed good separation of healthy gait subjects from those with trend or outliers, and furthermore the trend measurement was able to detect subtle decay in gait not easily discerned by the human eye.

Weight Estimation from an RGB-D Camera in Top-View Configuration

Marco Mameli, Marina Paolanti, Nicola Conci, Filippo Tessaro, Emanuele Frontoni, Primo Zingaretti

Auto-TLDR; Top-View Weight Estimation using Deep Neural Networks

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The development of so-called soft-biometrics aims at providing information related to the physical and behavioural characteristics of a person. This paper focuses on bodyweight estimation based on the observation from a top-view RGB-D camera. In fact, the capability to estimate the weight of a person can be of help in many different applications, from health-related scenarios to business intelligence and retail analytics. To deal with this issue, a TVWE (Top-View Weight Estimation) framework is proposed with the aim of predicting the weight. The approach relies on the adoption of Deep Neural Networks (DNNs) that have been trained on depth data. Each network has also been modified in its top section to replace classification with prediction inference. The performance of five state-of-art DNNs has been compared, namely VGG16, ResNet, Inception, DenseNet and Efficient-Net. In addition, a convolutional auto-encoder has also been included for completeness. Considering the limited literature in this domain, the TVWE framework has been evaluated on a new publicly available dataset: “VRAI Weight estimation Dataset”, which also collects, for each subject, labels related to weight, gender, and height. The experimental results have demonstrated that the proposed methods are suitable for this task, bringing different and significant insights for the application of the solution in different domains.

XGBoost to Interpret the Opioid Patients’ StateBased on Cognitive and Physiological Measures

Arash Shokouhmand, Omid Dehzangi, Jad Ramadan, Victor Finomore, Nasser M. Nasarabadi, Ali Rezai

Auto-TLDR; Predicting the Wellness of Opioid Addictions Using Multi-modal Sensor Data

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Dealing with opioid addiction and its long-term consequences is of great importance, as the addiction to opioids is emerged gradually, and established strongly in a given patient's body. Based on recent research, quitting the opioid requires clinicians to arrange a gradual plan for the patients who deal with the difficulties of overcoming addiction. This, in turn, necessitates observing the patients' wellness periodically, which is conventionally made by setting clinical appointments. However, this approach of dealing runs the risk of relapse for patients, as there would not be any monitoring between the clinical sessions. Thus, we need to increase the number of clinical appointments for opioid patients, which is not feasible due to the high financial costs, and the patients not having enough forbearance. Nevertheless, with the advent of wearable sensors continuous patient monitoring becomes possible. However, the data collected through the sensors is pervasively noisy, where using sensors with different sampling frequency challenges the data processing. In this work, we handle this problem by using 12-hour resolution data from cognitive tests, along with heart rate (HR) and heart rate variability (HRV), sampled at each 15 and 180 seconds, respectively. The proposed recipe enables us to interpret the multi-modal sensor data as a feature space, where we can predict the wellness of the opioid patients by employing extreme gradient boosting (XGBoost), which results in 96.12% average accuracy of prediction as the best achieved performance.

Exploring Spatial-Temporal Representations for fNIRS-based Intimacy Detection via an Attention-enhanced Cascade Convolutional Recurrent Neural Network

Chao Li, Qian Zhang, Ziping Zhao

Auto-TLDR; Intimate Relationship Prediction by Attention-enhanced Cascade Convolutional Recurrent Neural Network Using Functional Near-Infrared Spectroscopy

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The detection of intimacy plays a crucial role in the improvement of intimate relationship, which contributes to promote the family and social harmony. Previous studies have shown that different degrees of intimacy have significant differences in brain imaging. Recently, a few of work has emerged to recognise intimacy automatically by using machine learning technique. Moreover, considering the temporal dynamic characteristics of intimacy relationship on neural mechanism, how to model spatio-temporal dynamics for intimacy prediction effectively is still a challenge. In this paper, we propose a novel method to explore deep spatial-temporal representations for intimacy prediction by Attention-enhanced Cascade Convolutional Recurrent Neural Network (ACCRNN). Given the advantages of time-frequency resolution in complex neuronal activities analysis, this paper utilizes functional near-infrared spectroscopy (fNIRS) to analyse and infer to intimate relationship. We collect a fNIRS-based dataset for the analysis of intimate relationship. Forty-two-channel fNIRS signals are recorded from the 44 subjects' prefrontal cortex when they watched a total of 18 photos of lovers, friends and strangers for 30 seconds per photo. The experimental results show that our proposed method outperforms the others in terms of accuracy with the precision of 96.5%. To the best of our knowledge, this is the first time that such a hybrid deep architecture has been employed for fNIRS-based intimacy prediction.

Inferring Functional Properties from Fluid Dynamics Features

Andrea Schillaci, Maurizio Quadrio, Carlotta Pipolo, Marcello Restelli, Giacomo Boracchi

Auto-TLDR; Exploiting Convective Properties of Computational Fluid Dynamics for Medical Diagnosis

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In a wide range of applied problems involving fluid flows, Computational Fluid Dynamics (CFD) provides detailed quantitative information on the flow field, at various levels of fidelity and computational cost. However, CFD alone cannot predict high-level functional properties of the system that are not easily obtained from the equations of fluid motion. In this work, we present a data-driven framework to extract additional information, such as medical diagnostic output, from CFD solutions. The task is made difficult by the huge data dimensionality of CFD, together with the limited amount of training data implied by its high computational cost. By pursuing a traditional ML pipeline of pre-processing, feature extraction, and model training, we demonstrate that informative features can be extracted from CFD data. Two experiments, pertaining to different application domains, support the claim that the convective properties implicit into a CFD solution can be leveraged to retrieve functional information for which an analytical definition is missing. Despite the preliminary nature of our study and the relative simplicity of both the geometrical and CFD models, for the first time we demonstrate that the combination of ML and CFD can diagnose a complex system in terms of high-level functional information.

Facial Expression Recognition by Using a Disentangled Identity-Invariant Expression Representation

Kamran Ali, Charles Hughes

Auto-TLDR; Transfer-based Expression Recognition Generative Adversarial Network (TER-GAN)

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Facial Expression Recognition (FER) is a challenging task because many factors of variation such as pose, illumination, and identity-specific attributes are entangled with the expression information in an expressive face image. Recent works show that the performance of a FER algorithm can be improved by disentangling the expression information from identity features. In this paper, we present Transfer-based Expression Recognition Generative Adversarial Network (TER-GAN) that combines the effectiveness of a novel feature disentanglement technique with the concept of identity-invariant expression representation learning for facial expression recognition. More specifically, TER-GAN learns a disentangled expression representation by extracting expression features from one image and transferring the expression information to the identity of another image. To improve the feature disentanglement process, and to learn an identity-invariant expression representation, we introduce a novel expression consistency loss and an identity consistency loss that exploit expression and identity information from both real and synthetic images. We evaluated the performance of our proposed facial expression recognition technique by employing five public facial expression databases, CK+, Oulu-CASIA, MMI, BU-3DFE, and BU-4DFE, the latter being used for pre-training. The experimental results show the effectiveness of the proposed technique.

Video Face Manipulation Detection through Ensemble of CNNs

Nicolo Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, Stefano Tubaro

Auto-TLDR; Face Manipulation Detection in Video Sequences Using Convolutional Neural Networks

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In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usefulness of these tools in many fields, if used maliciously, they can have a significantly bad impact on society (e.g., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models. In the proposed solution, different models are obtained starting from a base network (i.e., EfficientNetB4) making use of two different concepts: (i) attention layers; (ii) siamese training. We show that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.