Silhouette-based gait representations are widely used in the current gait recognition community due to their effectiveness and efficiency, but they are subject to changes in covariate conditions such as clothing and carrying status. Therefore, we propose a gait energy response function (GERF) that transforms a gait energy (i.e., an intensity value) of a silhouette-based gait feature into a value more suitable for handling these covariate conditions. Additionally, since the discrimination capability of gait energies, as well as the degree to which they are affected by the covariate conditions, differs among body parts, we extend the GERF framework to spatially dependent GERF (SD-GERF) which accounts for spatial dependence. Moreover, the proposed GERFs are represented as a vector in the transformation lookup table and are optimized through an efficient generalized eigenvalue problem in a closed form. Finally, two post-processing techniques, Gabor filtering and spatial metric learning, are employed for the transformed gait features to boost the accuracy. Experimental results with three publicly available datasets including clothing and carrying status variations show the state-of-the-art performance of the proposed method compared with other state-of-the-art methods.
Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status