Fusion for Gait-based Human Recognition
Presented by: Ju Han
This work presents a novel approach for human recognition
by combining statistical gait features from real and synthetic
templates. Real templates are directly computed from training
silhouette sequences, while synthetic templates are generated
from training sequences by simulating silhouette distortion.
A statistical feature extraction approach is used for learning
effective features from real and synthetic templates. Features
learned from real templates characterize human walking properties
provided in training sequences, and features learned from
synthetic templates predict gait properties under other conditions.
A feature fusion strategy is therefore applied at the decision
level to improve recognition performance. We apply the proposed
approach to USF HumanID Database. Experimental results demonstrate
that the proposed fusion approach achieves not only better
performance than individual approaches, but also large performance
improvements with respect to the baseline algorithm.