Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Small Sample Face Recognition Algorithm Based on Novel Siamese Network

  • Zhang, Jianming (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation and School of Computer and Communication Engineering, Changsha University of Science and Technology) ;
  • Jin, Xiaokang (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation and School of Computer and Communication Engineering, Changsha University of Science and Technology) ;
  • Liu, Yukai (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation and School of Computer and Communication Engineering, Changsha University of Science and Technology) ;
  • Sangaiah, Arun Kumar (School of Computer Science and Engineering, Vellore Institute of Technology (VIT)) ;
  • Wang, Jin (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation and School of Computer and Communication Engineering, Changsha University of Science and Technology)
  • Received : 2018.08.31
  • Accepted : 2018.10.22
  • Published : 2018.12.31
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Abstract

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

Keywords

E1JBB0_2018_v14n6_1464_f0001.png 이미지

Fig.1 Siamese network algorithm architecture.

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Fig. 2. SiameseFacel network architecture.

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Fig. 3. SiameseFace2 network architecture.

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Fig. 4. AR dataset part of the face image.

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Fig. 5. LFW dataset part of the face image.

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Fig. 6. matched pairs and unmatched paris generation.

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Fig. 7. Different model loss convergence performance comparison chart.

Table 1. SiameseFacel network architecture.

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Table 2. SiameseFace2 network parameter

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Table 3. SiameseFace2 network parameter

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Table 4. Five different network model's configuration and recognition rate

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Table 5. Experimental results on the AR dataset

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Table 6. Experimental results on the LFW dataset

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Table 7. Comparison of different loss functions on AR dataset

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