In this paper, we propose a graph-based feature aggregation network (G-FAN) for video face recognition. Compared with the still image, video face recognition exhibits great challenges due to huge intra-class variability and high inter-class ambiguity. To address this problem, our G-FAN first uses a Convolutional Neural Network to extract deep features for every input face of a subject. Then, we build an affinity graph based on the relation between facial features and apply Graph Convolutional Network to generate fine-grained quality vectors for each frame. Finally, the features among multiple frames are adaptively aggregated into a discriminative vector to represent a video face. Different from previous works that take a single image as input, our G-FAN could utilize the correlation information between image pairs and aggregate a template of faces simultaneously. The experiments on video face recognition benchmarks, including YTF, IJB-A, and IJB-C show that: (i) G-FAN automatically learns to advocate high-quality frames while repelling low-quality ones. (ii) G-FAN significantly boosts recognition accuracy and outperforms other state-of-the-art aggregation methods.

This paper presents a novel method based on the generative adversarial network for radial distortion correction. Instead of generating a corrected image, our generator predicts a pixel flow map to measure the pixel offset between the distorted and corrected image. The quality of the generated pixel flow map and the warped image are judged by the discriminator. As texture far away from the image center has strong distortion, we develop an Adaptive Inverted Foveal layer which can transform the deformation to the intensity of the image to exploit this property. Rotation symmetry enhanced convolution kernels are applied to extract geometric features of different orientations explicitly. These learned features are recalibrated using the Squeeze-and-Excitation block to assign different weights for different directions. Moreover, we construct a first real-world radial distorted image dataset RD600 annotated with ground truth to evaluate our proposed method. We conduct extensive experiments to validate the effectiveness of each part of our framework. The further experiment shows our approach outperforms previous methods in both synthetic and real-world datasets quantitatively and qualitatively.