Authors: Jayant Jagtap; Manesh Kokare
Addresses: Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, 431606, India ' Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, 431606, India
Abstract: This paper presents a novel method for human age classification via face images by a computer. The proposed method classifies the human face images into four age groups: child, young, adult and senior adult by using appearance features as ageing features and artificial neural network (ANN) as age classifier. The appearance features consist of both shape and textural features. Only two geometric ratios in combination with newly introduced rotation, scale and translation invariant efficient feature face angle are used as shape features. Local binary pattern histogram (LBPH) of regions of interest in face images are used as textural features. The ANN is designed by using two layer feedforward backpropagation neural networks. The performance of proposed age classification system is evaluated on face images from FG-NET ageing database and achieved greatly improved accuracy of 91.09% and 88.18% for male and female, respectively.
Keywords: human age classification; appearance features; artificial neural networks; ANNs; local binary pattern histogram; LBPH; face images; biometrics; ageing features; shape features; textural features.
International Journal of Biometrics, 2016 Vol.8 No.3/4, pp.179 - 201
Available online: 27 Feb 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article