Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM–SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM–SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM–SPP outperforms several silhouette-based gait recognition methods.

► A method that analyses silhouette contours to classify human subjects.
► It uses Procreates shape analysis and elliptic Fourier descriptors (EFDs).
► Part-based EFDs are used for robustness against carrying conditions.
► Tie in combined classification result is resolved using contour matching.