Gender Classification Using Local Binary Patterns and its Variants

Many social interactions and services are dependent on gender today, so, gender classification is appearing as an active research area. Most of the existing studies are based on face images acquired under controlled conditions. In our work, we used different databases such as FERET, AR and ORL for controlled conditions and Labeled Faces in the Wild (LFW) database as real-life faces for uncontrolled conditions. Local Binary Patterns (LBP) and its variants such as Uniform LBP, Completed LBP and Rotation - Invariant LBP are employed to describe faces by extracting features from the region of interests. Manhattan distance measure is used to compare difference between test and training images for gender recognition. Based on the results reported as the state-of-the-art, we have achieved satisfactory results. Keywords: Gender recognition, feature extraction, Local Binary Patterns (LBP)