TY - GEN
T1 - FACE AGGREGATION NETWORK FOR VIDEO FACE RECOGNITION
AU - Hörmann, Stefan
AU - Cao, Zhenxiang
AU - Knoche, Martin
AU - Herzog, Fabian
AU - Rigoll, Gerhard
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Typical approaches for video face recognition aggregate faces in a feature space to obtain a single feature representing the entire video. Unlike most previous approaches, we aggregate the faces directly in order to additionally obtain a single representative face as an intermediate output, from which a more discriminative feature vector is extracted. To overcome the limitation of a fixed number of input images of the state of the art in face aggregation, we incorporate a permutation invariant U-Net architecture capable of processing an arbitrary number of frames, which is employed in a generative adversarial network. We demonstrate the effectiveness of our method on three popular benchmark datasets for video face recognition. Our approach outperforms the baselines on the YouTube Faces dataset, obtaining an accuracy of 96.62 %. Besides, we show that our method is robust against motion blur.
AB - Typical approaches for video face recognition aggregate faces in a feature space to obtain a single feature representing the entire video. Unlike most previous approaches, we aggregate the faces directly in order to additionally obtain a single representative face as an intermediate output, from which a more discriminative feature vector is extracted. To overcome the limitation of a fixed number of input images of the state of the art in face aggregation, we incorporate a permutation invariant U-Net architecture capable of processing an arbitrary number of frames, which is employed in a generative adversarial network. We demonstrate the effectiveness of our method on three popular benchmark datasets for video face recognition. Our approach outperforms the baselines on the YouTube Faces dataset, obtaining an accuracy of 96.62 %. Besides, we show that our method is robust against motion blur.
KW - Biometrics
KW - Face aggregation
KW - Generative adversarial network
KW - Video face recognition
UR - https://www.scopus.com/pages/publications/85125556021
U2 - 10.1109/ICIP42928.2021.9506037
DO - 10.1109/ICIP42928.2021.9506037
M3 - Conference contribution
AN - SCOPUS:85125556021
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2973
EP - 2977
BT - 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PB - IEEE Computer Society
T2 - 28th IEEE International Conference on Image Processing, ICIP 2021
Y2 - 19 September 2021 through 22 September 2021
ER -