A Graph Convolutional Neural Network for Reliable Gait-Based Human Recognition

Abstract

In a domain of human-machine autonomous systems, gait recognition provides unique advantages over other biometric modalities. It is an unobtrusive, widely-acceptable way of identity, gesture and activity recognition, with applications to surveillance, border control, risk prediction, military training and cybersecurity. Trustworthy and reliable person identification from videos under challenging conditions, when a subject’s walk is occluded by environmental elements, bulky clothing or a viewing angle, is addressed in this paper. It proposes a novel deep learning architecture based on Graph Convolutional Neural Network (GCNN) for accurate and reliable gait recognition from videos. The optimized feature map of the proposed GCNN architecture ensures that recognition remains accurate and invariant to viewing angle, type of clothing or other conditions.