Gait based Age Estimation using LeNet-50 inspired Gait-Net

Abstract

Gait is a behavioural biometric that does not require the subject’s collaboration as it can be captured at a distance. The Gait-based age estimation has extensive applications in surveillance, customer age estimation in shopping centres and malls for business intelligence purposes and age-constrained access control to places like liquor shops, etc. In this paper, we propose Gait-Net, a LeNet-50 inspired age classification Convolutional Neural Network (CNN) for Gait-based age estimation. We propose the application of a heat map filter on each Gait Energy Image (GEI), for the enhancement of age differentiating features in the GEI, subsequently followed by the sequential age group and age estimation CNN models. We addressed the inherent class imbalance problem induced by the non-availability of sufficient data for the elderly subjects, by using the image augmentation technique. We evaluated our model on the OU-ISIR Large Population Gait Database and the results confirmed its efficiency.