Gender classification system for half face images using multi manifold discriminant analysis

Recognizing the gender from the half face image is a challenging problem in the field of computer vision. This paper investigates the issue and proposes a gender classification system that works for full-face images to half face images. In this manuscript, a Discrete Wavelet Transform (DWT) followed by MMDA is used for feature extraction. The proposed approach uses DWT to gather the potential information from the face images. Support Vector Machine (SVM) and k-NN classifiers are used to finds the features that can discriminate between male and female. The proposed method is evaluated on FERET and FEI databases and the experimental result shows that the proposed technique achieves the gender classification target with more than 94% accuracy for both half face and full-face images.