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

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.

Title: Deep age estimation using sclera images in multiple environment.

Abstract:

Automatic human age estimation from images using machine learning techniques is a challenging task. The color and texture of sclera present in human eye change due to the physical aging process. In this work, we explore the effectiveness of using sclera region in eye images for age estimation. Our work uses a modified form of deep neural network model VGG-16. The model is trained and tested by the SBVPI dataset, in which images are acquired with high-end cameras. The model is also tested by images acquired by a mobile camera fitted with a macro lens. Though the work is in its initial stage, it gives the best mean-absolute-error of 0.06. Encouraging results lead us to conclude that sclera images can be used as an effective modality for human age estimation.