Face Recognition by PCA and Improved LBP Fusion Algorithm

Abstract:

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

• Periodical: Applied Mechanics and Materials (Volume 734)
• Pages: 562-567
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.734.562
• Online since: February 2015
• Authors: En Zeng Dong, Yan Hong Fu*, Ji Gang Tong
• Keywords: Face Recognition, Nearest Distance Classification, Principal Component Analysis (PCA), Rotation Invariant Uniform Local Binary Patterns (LBP)

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