Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions

  • Li, Chen (School of Computer Science, North China University of Technology) ;
  • Zhao, Shuai (School of Computer Science, North China University of Technology) ;
  • Xiao, Ke (School of Computer Science, North China University of Technology) ;
  • Wang, Yanjie (School of Computer Science, North China University of Technology)
  • Received : 2017.05.08
  • Accepted : 2017.08.27
  • Published : 2018.02.28
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Abstract

To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.

Keywords

E1JBB0_2018_v14n1_191_f0001.png 이미지

Fig. 1. Encoding process of the CSLBP descriptor.

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Fig. 2. Wavelet decomposition of images under normal illumination (a) and severe illuminationvariation (b).

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Fig. 3. Wavelet decomposition after nonlinear grayscale enhancement.

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Fig. 4. Fusion process.

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Fig. 5. DBN structure of three-layer RBM model.

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Fig. 6. Illustration of the DBN framework.

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Fig. 7. Examples of CMU-PIE face image.

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Fig. 8. Recognition rate comparison on CMU-PIE database.

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Fig. 9. First experiment scheme on the Extended Yale-B database.

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Fig. 10. Results comparison of the first experiment on Extended Yale-B database.

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Fig. 11. Second experiment scheme on the Extended Yale-B database.

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Fig. 12. Results comparison of the second experiment on Extended Yale B database.

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