Vision-Based Hand Gesture Recognition Using Combinational Features

This paper presents a feature extraction method for hand gesture based on multi-layer perceptron. The feature of hand skin color in the YCbCr color space is used to detect hand gesture. The hand silhouette and features can be accurately extracted in means of binarizing the hand image and enhancing the contrast. Median and smoothing filters are integrated to remove the noise. Combinational parameters of Hu invariant moment, hand gesture region, and Fourier descriptor are created to form a new feature vector which can recognize hand gesture. To confirm the robustness of this proposed method, a dataset including 3500 images is built. Experimental results demonstrate that our system can successfully recognize hand gesture with 97.4% recognition rate.