Gait refers to an individual’s unique walking pattern and is a promising biometric for age and gender estimation. Human gait exhibits inherent bilateral symmetry arising from the coordinated movement of the left and right sides of the body. However, occlusion remains a major challenge that disrupts the symmetric structure of gait patterns and degrades recognition performance. This paper investigates the impact of different occlusion types on gait-based age and gender estimation and proposes a Generative Adversarial Network (GAN)-based image restoration model to mitigate occlusion effects. Two occlusion types, namely block-wise and component-specific, are examined. A self-collected dataset of 715 side-walking gait energy images (GEIs) from 120 subjects was synthetically occluded to simulate real-life scenarios. Block-wise occlusion was applied both vertically and horizontally across GEI silhouettes, while component-specific occlusion targeted individual body parts. GAN-based restoration was subsequently applied to occluded images prior to model training. Experimental results confirm that occlusion significantly degrades recognition accuracy, with larger occluded regions causing greater performance drops. Shoulder occlusion most severely impacted age estimation, while head occlusion had the greatest effect on gender estimation. GAN-based restoration substantially recovered lost accuracy, demonstrating the potential of restoration techniques in compensating for missing body information. These findings highlight the importance of upper-body regions in gait-based soft biometrics and demonstrate the need to address occlusion in real-world gait recognition systems.
Zheng et al. (Thu,) studied this question.