Gait recognition using binarized statistical image features and histograms of oriented gradients

This paper presents a gait recognition method using the combination of motion history image (MHI), binarized statistical image features (BSIF) and histograms of oriented gradients (HOG). The method first encodes the motion pattern and direction of the gait cycle in motion history image. Subsequently, performing convolution on the motion history image using pre-learnt filters as kernel, binarized statistical image features are generated by summing the convolution output images. Histograms of oriented gradients are then computed on binarized statistical image features. Gait signature of a gait cycle is attained by accumulating all the HOG descriptors. Experimental result shows that the proposed method performs promisingly in gait recognition.