Title: An investigation into automated age estimation using sclera images: a novel modality

Authors: Sumanta Das; Ishita De Ghosh; Abir Chattopadhyay

Addresses: University of Engineering and Management, University Area, Plot No. III, B/5, New Town Rd., Action Area III, Newtown, Kolkata, West Bengal, 700160, India ' Barrackpore Rastraguru Surendranath College, 85, Middle Road, 6, River Side Rd., Kolkata, West Bengal, 700120, India ' University of Engineering and Management, University Area, Plot No. III, B/5, New Town Rd., Action Area III, Newtown, Kolkata, West Bengal, 700160, India

Abstract: Automated age estimation attracts attention due to its potential application in fields like customer relationship management, surveillance, and security. Ageing has a significant effect on human eye, particularly in the sclera region, but age estimation from sclera images is a less explored topic. This work presents a comprehensive investigation on automated human age estimation from sclera images. We employ light-weight deep learning models to identify the changes in the sclera colour and texture. Extensive experiments are conducted for three related tasks: estimation of exact-age of a subject, categorical classification of subjects in different age-groups, and binary classification of adult and minor subjects. Results demonstrate good performance of the proposed models against the state-of-the-art methods. We have obtained mean-absolute-error of 0.05 for the first task, accuracy of 0.92 for the second task, and accuracy of 0.89 for the third task.

Keywords: human age estimation; age-group classification; adult-minor binary classification; sclera images; deep learning; MASDUM; SBVPI.

DOI: 10.1504/IJCVR.2024.135127

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.1, pp.42 - 62

Received: 19 Jan 2022
Accepted: 24 Jun 2022
Published online: 01 Dec 2023 *