Cross Teager-Kaiser Energy Operator based Feature Extraction Method for Gait Recognition from Cumulative Foot Pressure Images

摘要

The human footprint is a convenient biometric to be used for recognition purposes as it is universal, easy to acquire and does not change much over time. The cumulative foot pressure image (CFPI) records the spatial and temporal changes of the ground reaction force over a single gait cycle, thus offering more information than an ordinary footprint. This enables us to distinguish between various human gait aspects like limb movement. In this paper a scheme is proposed for gait recognition, that exploits features extracted from the cumulative foot pressure images (CFPIs) collected from 88 subjects, using the Cross Teager-Kaiser Energy Operator (CTKEO). This scheme exhibits a consistency in performance across major classifiers, while delivering a maximum recognition accuracy of 97.3%.