Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Multi-Scale Parallel Convolutional Neural Network Based Intelligent Human Identification Using Face Information

  • Li, Chen (School of Computer Science, North China University of Technology) ;
  • Liang, Mengti (School of Computer Science, North China University of Technology) ;
  • Song, Wei (School of Computer Science, North China University of Technology) ;
  • Xiao, Ke (School of Computer Science, North China University of Technology)
  • Received : 2018.09.07
  • Accepted : 2018.10.22
  • Published : 2018.12.31
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Abstract

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

Keywords

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Fig. 1. The structure of MP-CNN.

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Fig. 2. Average pooling (a) and max pooling (b).

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Fig. 3. 1-CNN structure.

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Fig. 4. 4-CNN structure.

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Fig. 5. Image examples of CMU-PIE face database.

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Fig. 6. Experiment results comparison on CMU-PIE database.

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Fig. 7. Image examples of the extended FERET face database.

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Fig. 8. Experiment results comparison on the extended FERET database.

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Fig. 9.The recognition performance of the five methods on expanded AR database are shown in Fig. 10.

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Fig. 9. Image examples of the extended AR face database.

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Fig. 10. Experiment results comparison on the extended AR database.

Table 1. The RANK1 recognition rates on CMU-PIE face database

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Table 2. The RANK1 recognition rates on extended FERET face database

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Table 3. The RANK1 recognition rates on the enhanced AR database

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