  Human can be distinguished by different limb movements and unique ground
reaction force. Cumulative foot pressure image is a 2-D cumulative ground
reaction force during one gait cycle. Although it contains pressure spatial
distribution information and pressure temporal distribution information, it
suffers from several problems including different shoes and noise, when putting
it into practice as a new biometric for pedestrian identification. In this
paper, we propose a hierarchical translation-invariant representation for
cumulative foot pressure images, inspired by the success of Convolutional deep
belief network for digital classification. Key contribution in our approach is
discriminative hierarchical sparse coding scheme which helps to learn useful
discriminative high-level visual features. Based on the feature representation
of cumulative foot pressure images, we develop a pedestrian recognition system
which is invariant to three different shoes and slight local shape change.
Experiments are conducted on a proposed open dataset that contains more than
2800 cumulative foot pressure images from 118 subjects. Evaluations suggest the
effectiveness of the proposed method and the potential of cumulative foot
pressure images as a biometric.
