1 research outputs found
The influence of segmentation on individual gait recognition
The quality of the extracted gait silhouettes can
hinder the performance and practicability of gait recognition
algorithms. In this paper, we analyse the influence of silhouette
quality caused by segmentation disparities, and propose a feature
fusion strategy to improve recognition accuracy. Specifically, we
first generate a dataset containing gait silhouette with various
qualities generated by different segmentation algorithms, based
on the CASIA Dataset B. We then project data into an embedded
subspace, and fuse gallery features of different quality levels.
To this end, we propose a fusion strategy based on Least
Square QR-decomposition method. We perform classification
based on the Euclidean distance between fused gallery features
and probe features. Evaluation results show that the proposed
fusion strategy attains important improvements on recognition
accuracy