Title: Human age classification using appearance and facial skin ageing features with multi-class support vector machine
Authors: Jayant Jagtap; Manesh Kokare
Addresses: Department of Electronics and Telecommunication Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Lavale, Pune, 412115, India ' Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, 431606, India
Abstract: Human age classification via face images is not only difficult for human being but also challenging for a machine. But, because of potential applications in the field of computer vision, this topic has attracted attention of many researchers. In this paper, a novel two stage age classification framework based on appearance and facial skin ageing features with multi-class support vector machine (M-SVM) is proposed to classify the face images into seven age groups. Appearance features consist of shape features such as, geometric ratios and face angle and facial skin textural features extracted by using local Gabor binary pattern histogram (LGBPH). Facial skin ageing features consist of facial skin textural features and wrinkle analysis. The proposed age classification framework is trained and tested with face images collected from FG-NET ageing database and PAL face database and achieved greatly improved age classification accuracy of 94.45%.
Keywords: appearance features; facial skin ageing features; local Gabor binary patterns histogram; LGBPH; wrinkle analysis; age classification framework; multi-class support vector machine; M-SVM.
International Journal of Biometrics, 2019 Vol.11 No.1, pp.22 - 34
Available online: 11 Oct 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article