Clothing and carrying condition invariant gait recognition based on rotation forest

•Introduces averaged gait key-phase image (AGKI) for gait recognition.•Recognition is robust to unpredictable variation in clothing and carrying conditions.•AGKIs are analysed using high-pass and low-pass Gaussian filters at different cut-off frequencies.•Optimal cut-off frequencies are chosen based on focus value analysis.•The method uses rotation forest ensemble method for classification.

This paper proposes a gait recognition method which is invariant to maximum number of challenging factors of gait recognition mainly unpredictable variation in clothing and carrying conditions. The method introduces an averaged gait key-phase image (AGKI) which is computed by averaging each of the five key-phases of the gait periods of a gait sequence. It analyses the AGKIs using high-pass and low-pass Gaussian filters, each at three cut-off frequencies to achieve robustness against unpredictable variation in clothing and carrying conditions in addition to other covariate factors, e.g., walking speed, segmentation noise, shadows under feet and change in hair style and ground surface. The optimal cut-off frequencies of the Gaussian filters are determined based on an analysis of the focus values of filtered human subject’s silhouettes. The method applies rotation forest ensemble learning recognition to enhance both individual accuracy and diversity within the ensemble for improved identification rate. Extensive experiments on public datasets demonstrate the efficacy of the proposed method.