Gesture recognition using HLAC features of PARCOR images

This paper proposes a gesture recognition method which uses higher order local autocorrelation (HLAC) features extracted from PARCOR images. To extract dominant information from a sequence of images, we apply linear prediction coding technique to the sequence of pixel intensities and PARCOR images are constructed from the PARCOR coefficients of the sequences of the pixel values. From the PARCOR images, HLAC features are extracted and the sequences of the features are used as the input vectors of the Hidden Markov Model (HMM) based recognizer. Since HLAC features are inherently shift-invariance and computationally inexpensive, the proposed method becomes robust to changes in the person's position and makes real-time gesture recognition possible. Experimental results of gesture recognition are shown to evaluate the performance of the proposed method.