Age classification based on back-propagation network

Authors:
Ying Zheng;Hongxun Yao;Yanhao Zhang;Pengfei Xu
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

In this paper, a novel approach is proposed for age classification of face images. In consideration of the difference of adolescents and adults in aging mode, we utilize the facial feature ratios to classify face images into two groups: juveniles and adults. To eliminate the uncertainty lying in the face images, we elaborate a preprocessing procedure to the face images. Then, the Local Binary Pattern (LBP), which is a powerful texture description methods, will be used to describe the appearance of face images based the preprocessed images. Finally, a back-propagation (BP) network is learned automatically by facial LBP features and predetermined outputs. Through this method, we accomplish the task of age classification well, which is a problem of nonlinear system. Given a face image of an uncertain age, the age group will be predicted by the learned BP network. Our experimental results indicate that our approach can achieve the goal of age classification well. Besides, the influence of gender is studied and we find that considering gender independently in age classification is advisable.