Cross-Cultural Facial Gender Recognition for Eastern and Western Populations

This research focuses on facial gender recognition across diverse ethnic populations, including both Eastern and Western demographics. Our approach implements a Local Gabor Binary Pattern Histogram Sequence (LGBPHS) based algorithm for gender classification. The methodology involves extracting Gabor-filtered features from facial regions, computing local binary patterns, and constructing histogram sequences for robust pattern recognition. This technique demonstrates high gender identification accuracy while maintaining minimal sensitivity to image noise, varying illumination conditions, and facial pose variations. The system achieves particularly impressive results for Eastern populations, reaching 96% recognition accuracy. Implementation typically involves OpenCV or MATLAB for Gabor filtering and histogram computation, with support vector machines (SVM) for classification. This research represents significant progress in gender recognition technology and provides substantial support for advancing facial recognition systems.