• Title/Summary/Keyword: Video Face Recognition

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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;You, Wonsang;Choi, Jae Young
    • Journal of Korea Multimedia Society
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    • v.24 no.10
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    • pp.1380-1390
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    • 2021
  • 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.

Misclassified Samples based Hierarchical Cascaded Classifier for Video Face Recognition

  • Fan, Zheyi;Weng, Shuqin;Zeng, Yajun;Jiang, Jiao;Pang, Fengqian;Liu, Zhiwen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.2
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    • pp.785-804
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    • 2017
  • Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

A Real-time Face Recognition System using Fast Face Detection (빠른 얼굴 검출을 이용한 실시간 얼굴 인식 시스템)

  • Lee Ho-Geun;Jung Sung-Tae
    • Journal of KIISE:Software and Applications
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    • v.32 no.12
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    • pp.1247-1259
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    • 2005
  • This paper proposes a real-time face recognition system which detects multiple faces from low resolution video such as web-camera video. Face recognition system consists of the face detection step and the face classification step. At First, it finds face region candidates by using AdaBoost based object detection method which have fast speed and robust performance. It generates reduced feature vector for each face region candidate by using principle component analysis. At Second, Face classification used Principle Component Analysis and multi-SVM. Experimental result shows that the proposed method achieves real-time face detection and face recognition from low resolution video. Additionally, We implement the auto-tracking face recognition system using the Pan-Tilt Web-camera and radio On/Off digital door-lock system with face recognition system.