An Extended Local Binary Pattern for Gender Classification
AuthorRoayaei Ardakany, Abbas
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The face is one of the most important biometric features of humans, conveying race, identity, age, gender and facial expression information, among which gender plays a significant role in social interactions. An automatic gender recognition system has many applications in computer-human interaction, psychology, security, demographic and business issues. In this work, we designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, we proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. We have evaluated our approach on the standard FERET and CAS-PEAL databases and our experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. Our results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy.