Abstract
Facial image analysis is an important and profound research in the field of computer vision. The prime issue of the face recognition is to develop the robust descriptors that discriminate facial features. In recent years, the local binary pattern (LBP) has attained a big attention of the biometric researchers, for facial image analysis due to its robustness shown for the challenging databases. This paper presents a novel method for facial image representation using local binary pattern, called augmented local binary pattern (A-LBP) which works on the consolidation of the principle of locality of uniform and non-uniform patterns. It replaces the non-uniform patterns with the mod value of the uniform patterns that are consolidated with the neighboring uniform patterns and extract pertinent information from the local descriptors. The experimental results prove the efficacy of the proposed method over LBP on the publicly available face databases, such as AT & T-ORL, extended Yale B, and Yale A.
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Acknowledgment
The authors acknowledge the Institute of Engineering and Technology (IET), Lucknow, Uttar Pradesh Technical University (UPTU), Lucknow for their financial support to carry out this research under the Technical Education Quality Improvement Programme (TEQIP-II) grant.
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Shyam, R., Singh, Y.N. (2016). Analysis of Local Descriptors for Human Face Recognition. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_27
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DOI: https://doi.org/10.1007/978-81-322-2538-6_27
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