Principal Component Analysis for Gesture Recognition Using SystemC

An algorithm for optimizing the Principal Component Analysis in gesture recognition is proposed, which makes use of covariance between factors to reduce data dimensions. The objectivity and automatization of above manual observation is realized by algorithm. We present an approach for the detection and identification of human gestures and describe a working, near gesture recognition system and then recognize the person by comparing characteristics of the gesture to those of known individuals. Our approach treats gesture recognition as a two dimensional recognition problem, taking advantage of the fact that gestures are normally upright and thus may be described by a small set of 2-D characteristics values. With minimal additional effort PCA provides a roadmap for how to reduce a complex data set to a lower dimension to reveal the sometimes hidden, simplified structure that often underlie it. The proposed algorithm is implemented in SystemC language, with the intention to download on to a FPGA. The output of the system developed in SystemC consists of the gesture ID with closest match, as well as value representing how close this match is (Euclidean Distance value). As PCA has been mostly used for face recognition, this technique has been extended to gesture recognition and is quite fast, relatively simple, and has been shown to work well in a somewhat constrained environment.