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

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.