Higher order local autocorrelation features of PARCOR images for gesture recognition

This paper proposes a feature extraction method for gesture recognition, which is based on higher order local autocorrelation (HLAC) of PARCOR images. To extract dominant information from a sequence of images, we apply linear prediction coding to the sequence of pixel values and PARCOR images are constructed from the PARCOR coefficients of the sequences of the pixel values. Then HLAC features, which are inherently shift-invariant and computationally inexpensive, are extracted from the PARCOR images. Thus the proposed features become robust to changes of shift of the person´s position. Experimental results of gesture recognition are shown to evaluate the performance of the proposed features