Gait recognition with Transient Binary Patterns

In this work, we present a combination of spatiotemporal approach and texture descriptors to extract the temporal patterns in gait cycles. Unlike most conventional methods that focus on spatial information while limiting temporal information captured, spatiotemporal methods preserve both spatial and temporal information. Inspired by the success of texture descriptors in face recognition, the proposed method likewise constructs texture descriptors of gait motion over time. For each gait cycle, the pixel-wise binary patterns along the temporal axis, referred to as the Transient Binary Patterns (TBP), is analyzed. These pixel-wise TBPs are then grouped into regional blocks from which we construct regional TBP histograms. These regional TBP histograms collectively form the global TBP histogram that represents both the distribution of temporal patterns and spatial location. Experimental results clearly show the superiority of the proposed approach over other considered methods.