Gender Classification and Age Detection Based on Human Facial Features Using Multi- Class SVM

Abstract

Gender classification is a binary classification problem, which can be stated as inferring female or male from a collection of facial images. Although there exist different methods for gender classification, such as gait, iris, hand shape and hair, yet the prominent methods to achieve the goal is based on facial features. In this paper, novel methodologies has been proposed to achieve the goal of (1) gender classification and (2) age detection in three step process. Firstly, input image set are pre- processed to perform noise removal, histogram equalization, size normalization and then face detection is performed. Secondly, Feature Extraction from facial image is performed. Finally to evaluate the performance of the proposed algorithm, experiments have been performed on various image set that contain equal proportion of male and female by using suitable binary SVM classifier which will classify the data set into two categories i.e male or female. To achieve the second goal, Multi- class SVM have been employed which will generate three classes i.e child, adult and old. The age of the input images are detected and classified into one of the three category.