An Efficient Age Estimation System based on Multi Linear Principal Component Analysis

Abstract

Problem statement: Human age estimation is an active research topic in computer vision. A vital characteristic in establishing identity of the person is the age. Age estimation from face images continues to be an extremely challenging task compared to other cognition problems. Age is a crucial factor in ascertaining the identity of a person. Age is estimated from the human face images available in the database by existing systems. They cannot estimate the age of an unknown person. Approach: A new age estimation system was developed to estimate the age of humans from their facial images. The proposed system consists of three processes. Face detection was the first process that normalizes the facial images. The next process extracts shape feature, frequency feature, texture feature and color feature for age estimation. Then the age is estimated using Multilinear Principal Component Analysis (MPCA). Results: The efficiency of the proposed system was compared with single, two and three features extracted from the facial image using FGNET and Indian Database. It is compared with SVR. The proposed method for Indian database gives the highest performance with MAE reduced by two years and with error tolerance of 10 ages’s estimation rate increased by 10% when compared with support vector regression method using three features extraction. Conclusion: The efficiency of the system is found to be increased for three features with Indian database from the comparative analysis. The implementation results illustrates that this age estimation process effectively estimates the age from the known and unknown person’s facial image with remarkable mean absolute error and cumulative scores.