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Attention Deep Neural Networks Learning based on Multiple Loss functions for Video Face Recognition

비디오 얼굴인식을 위한 다중 손실 함수 기반 어텐션 심층신경망 학습 제안

  • Kim, Kyeong Tae (Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies) ;
  • You, Wonsang (Dept. of Information and Communications Engineering, Sun Moon University) ;
  • Choi, Jae Young (Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies)
  • 김경태 ;
  • 유원상 ;
  • 최재영
  • Received : 2021.08.12
  • Accepted : 2021.10.12
  • Published : 2021.10.30

Abstract

The video face recognition (FR) is one of the most popular researches in the field of computer vision due to a variety of applications. In particular, research using the attention mechanism is being actively conducted. In video face recognition, attention represents where to focus on by using the input value of the whole or a specific region, or which frame to focus on when there are many frames. In this paper, we propose a novel attention based deep learning method. Main novelties of our method are (1) the use of combining two loss functions, namely weighted Softmax loss function and a Triplet loss function and (2) the feasibility of end-to-end learning which includes the feature embedding network and attention weight computation. The feature embedding network has a positive effect on the attention weight computation by using combined loss function and end-to-end learning. To demonstrate the effectiveness of our proposed method, extensive and comparative experiments have been carried out to evaluate our method on IJB-A dataset with their standard evaluation protocols. Our proposed method represented better or comparable recognition rate compared to other state-of-the-art video FR methods.

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