Abstract
This paper proposes a local texture feature descriptor which fuses the center pixel information into the Center-Symmetric Local Binary Pattern (CS-LBP) for the purpose of face recognition. Because of its tolerance to illumination changes, and computational efficiency, the CS-LBP is widely used in face recognition. But this operator completely ignores the center pixel information which may affect the discriminative result in some applications. In order to take advantage of more useful information, this paper fuses the center pixel information into CS-LBP descriptor, namely CS-LBP/Center. In face recognition, the face image is first divided into small blocks from which CS-LBP/Center histograms are extracted and then weighted by image entropy. Finally, all the weighted histograms are connected serially to create a final texture descriptor for face recognition. The experimental results on some face datasets show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification.
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The authors wish to thank the Associate Editor and the anonymous reviewers for their helpful comments and valuable suggestions regarding this paper.
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This work is supported by State Key Laboratory of Smart Grid Protection and Control of China and the National Natural Science Foundation of China No. 61170322, No. 61373065 and No. 61302157
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Zhou, N., Constantinides, A.G., Huang, G. et al. Face recognition based on an improved center symmetric local binary pattern. Neural Comput & Applic 30, 3791–3797 (2018). https://doi.org/10.1007/s00521-017-2963-2
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DOI: https://doi.org/10.1007/s00521-017-2963-2