Gender Classification from Facial Images using PCA and SVM

Abstract

Biometrics is the use of physical characteristics like face, fingerprints, iris etc. of an individual for personal identification. Some of the challenging problems of face biometrics are face detection, face recognition, and face identification. These problems are being researched by the computer vision community for the last few decades. Considering the large population, the authentication process of an individual usually consumes a significant amount of time. One of the possible solutions is to divide the population into two halves based on gender. This will help to reduce the search space of authentication to almost half of the existing data and save substantial amount of time. Gender identification through face demands use of strong discriminative features and robust classifiers to separate the female and male faces without any ambiguity. In this thesis, an investigation has been made on gender classification through facial images using principal component analysis (PCA), and support vector machine (SVM). PCA is a dimensionality reduction technique, which is used to represent each image as a feature vector in a low dimensional subspace. SVM is a binary classifier for which PCA is the input in the form of features and predicts which of the two possible classes forms the output. Initially face region is extracted using a proposed skin colour segmentation approach. The face region is then subjected to PCA for feature extraction, which encodes second order statistics of data. These principal components are fed as input to SVM for classification.