Spline magnitude disparity cross correlated deep network for gait recognition

Abstract

Gait recognition stands as a pivotal biometric technology in individual identification, yet its real-world implementation faces challenges stemming from intra-subject disparities. The task of extracting consistent features to distinguish among various subjects becomes onerous due to factors such as image noise and magnitude divergence, significantly impacting recognition accuracy. In addressing this hurdle, we introduce a groundbreaking approach known as the Spline Magnitude Disparity Cross-Correlated Deep Network, designed to optimize gait recognition efficiency. Our method, the Spline Magnitude Disparity Cross-Correlated Deep Network, operates through two key steps: B-Spline magnitude disparity deformation (BS-MDD) registration and cross-correlated long-short gait recognition modeling. The BS-MDD algorithm employs free-form deformation to approximate the magnitude divergence in gait input, enhancing viewpoint optimization and contributing to the development of the cross-correlated model. By focusing on preserving high-output recognition gates while eliminating forget gates, our approach achieves a heightened recognition rate. Evaluation on the widely utilized CASIA B dataset showcases the superiority of our proposed method over state-of-the-art alternatives in terms of the true positive rate, false-positive rate, recognition time, and overall recognition rate. Notably, our approach elevates the true positive rate by 5% and reduces the false-positive rate by 4%. These results underscore the high effectiveness of our method, demonstrating its capacity to substantially improve the accuracy of gait recognition in practical applications.”