Model-Based Gait Recognition Using Multiple Feature Detection

This paper presents a gait recognition algorithm for human identification from a sequence of segmented noisy silhouettes in a low-resolution video. The main contribution of the proposed work is the use of the hierarchical recovery of a static body and stride parameters of model subjects to the walking pose. The proposed algorithm overcomes drawbacks of existing works by extracting a set of relative model parameters instead of directly analyzing the gait pattern. The feature extraction function in the proposed algorithm consists of motion detection, object region detection, and active shape model (ASM), which alleviate problem in the baseline algorithm such as; background generation, shadow removal, and higher recognition rate. Performance of the proposed algorithm has been evaluated by using the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition, and time.