Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU)

Abstract

Human beings can easily categorize a person’s age group from a facial image where as this ability has not been promising in the computer vision community. To address this problem very important area of research, the present paper proposes a novel scheme of age classification system using features derived from co-occurrence parameters using Rank based Edge Texture Unit (RETU). The Co-occurrence Matrix (CM) on RETU characterizes the relationship between the neighboring edge values, while preserving local information. The novelty of the proposed RETU is it classifies the age of human into seven categories i.e. in the age groups of 1–10, 11–20, 21–30, 31–40, 41–50, 51–60, and greater than 60. The TU of the proposed RETU ranges from 0 to 17 and thus reduces overall complexity in evaluating features from CM. The co-occurrence features extracted from the RETU provide complete facial image information for age classification purpose. The RETU reduces each 3 × 3 sub image into 2 × 2 sub image while preserving the texture features and thus reduces the overall dimensionality of the image.