Affiliations: [a] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
| [b]
AVIC Xi’an Aeronautics Computing Technique Research Institute, Xian, Shanxi, China
Correspondence:
[*]
Corresponding author. Haitao Wang, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China. E-mail: [email protected].
Note: [†] Huaibin Wang and Luyang Zhang contributed equally to this article.
Abstract: Unconstrained video face recognition is an extension of face recognition technology, and it is an indispensable part of intelligent security and criminal investigation systems. However, general face recognition technology cannot be directly applied to unconstrained video face recognition, because the video contains fewer frontal face image frames and a single image contains less face feature information. To address the above problems, this work proposes a Feature Map Aggregation Network (FMAN) to achieve unconstrained video face recognition by aggregating multiple face image frames. Specifically, an image group is used as the input of the feature extraction network to replace a single image to obtain a multi-channel feature map group. Then a quality perception module is proposed to obtain quality scores for feature maps and adaptively aggregate image features from image groups at the feature map level. Finally, extensive experiments are conducted on the challenging face recognition benchmarks YTF, IJB-A and COX to evaluate the proposed method, showing a significant increase in accuracy compared to the state-of-the-art.
Keywords: Video face recognition, Aggregation, Deep convolutional neural network, Feature map
