Gait biometrics: investigating the use of the lower inner regions for people identification from landmark frames

Abstract

The recent technological advances in surveillance, forensic and biometric systems to deter or even reduce the increasing number of crimes and prevent them is still questionable. The use of gait biometrics has attracted unprecedented interest due to its capability to work with low-resolution footage recorded from a distance. In contrast to mainstream research on gait biometrics which uses holistic silhouette features, the authors investigate the use of the bottom dynamic section within the human body to derive the most discriminative features for gait recognition. A new descriptor based on 7 Hu's moments is proposed describing the inner lower limb regions between the limbs being extracted only from landmark frames within one gait cycle. In order to assess the discriminatory potency of gait features from the lower regions for people identification, a number of experiments are conducted on the CASIA-B gait database to investigate the recognition rates using the KNN classifier and deep learning. The comparative analysis is performed against well-established research studies which were tested on the CASIA-B data set. The obtained results confirm the consistency of features extracted from the lower regions for gait recognition even under the impact of various factors.