Authors: Santhosh Kumar Gangadharaih; H.N. Suresh
Addresses: East West Institute of Technology, No. 63, Anjananagar, Off Magadi Road, Near BEL Layout, Bengaluru, Karnataka 560091, India ' Bangalore Institute of Technology, KR Road, VV Puram, Bengaluru, Karnataka 560004, India
Abstract: Age estimation (AE) is one of the significant biometric behaviours for emphasising the identity authentication. In facial image, automatic-AE is an actively researched topic, which is also an important but challenging study in the field of face recognition. This paper explores several algorithms utilised to improve AE and the combination of features and classifiers are associated. Initially, the facial image databases are trained and then the features are extracted by employing several algorithms like histogram of oriented gradients (HOG), binary robust invariant scalable keypoints (BRISK), and local binary pattern (LBP). Here, the AE is done in three various age groups from 20 to 30, 31 to 50 and above 50. The age groups are classified by utilising Naïve Bayes classifier (NBC). AE model is calculated by employing the Indian face age database (IFAD) and labelled Wikipedia face (LWF) aging databases obtaining optimistic result with success rate.
Keywords: age estimation; binary robust invariant scalable keypoints; histogram of oriented gradients; local binary pattern; Naïve Bayes classifier.
International Journal of Computer Aided Engineering and Technology, 2020 Vol.12 No.1, pp.17 - 32
Received: 26 Feb 2017
Accepted: 19 May 2017
Published online: 27 Nov 2019 *