Automatic Age Prediction of Aging Effects on Face Images

Abstract

Automatic age prediction system for grayscale facial images is proposed in this paper. Ten age groups, including, are used in the prediction system. The process of the system is divided into three phases: location, feature extraction, and age prediction. Principal Component Analysis (PCA) was used to reduce dimension and enhance class. Finally Euclidean distance was used to classify the images into one of seven major groups. These groups are: Group1 (0 to 10 years), Group2 (11 to 20 years), Group3 (21 to 30 years), Group4 (31 to 40 years), Group5 (41 to 50 years), Group6 (51 to 60 years) and Group7 (60 over). The proposed system is experimented with 1300 facial images on a Core 2 Duo processor with 2 GB RAM. One half of the images are used for training and the other half for test. It takes 0.2 second to classify an image on an average. The identification rate achieves 95.5% for the training images and 85.5% for the test images, which is roughly close to human’s subjective prediction.