Assessment of gait recognition based on the lower part of the human body

This paper is focused on the assessment of gait recognition on a constrained scenario, where limited information can be extracted from the gait image sequences. In particular we are interested in assessing the performance of gait images when only the lower part of the body is acquired by the camera and just half of a gait cycle is available (SFootBD database). Thus, various state-of-the-art feature approaches have been followed and applied to the data. A comparison with a standard and ideal gait database (USF database) is also carried out using similar experimental protocols. Results show that good recognition performance can be achieved using such limited data information for gait biometric (around 85% of rank 5 identification rate and 8.6% of EER). The comparison with a standard database shows that different feature approaches perform differently for each database, achieving best individual results with MPCA and EGEI methods for the SFootBD and the USF database respectively.