Optimized Features Selection using Hybrid PSOGA for Multi-View Gender Classification

Gender classification is a fundamental face analysis task. In literature, the focus of most researchers has been on the face images acquired under controlled conditions. Real-world face images contain different illumination effects and variations in facial expressions and poses that make gender classification more challenging task. In this paper, we have proposed an efficient gender classification technique for real world face images (Labeled faces in the Wild). After extracting both global and local features using Discrete Cosine Transform (DCT) and Local Binary Pattern (LBP), we have fused these features. Proposed algorithm provides support for variations in expressions and poses. To reduce the data dimensions, fused features are passed to hybrid PSO-GA that eliminates irrelevant features and results in optimized features. Support Vector Machine (SVM) is trained and tested by using optimized features. Using this approach we have received a 98% accuracy rate. We are utilizing the minimum number of features so our technique is faster as compared to other state-of-the-art gender classification techniques