Hybrid feature selection for gesture recognition using support vector machines

This paper presents an algorithm for extracting and classifying two-dimensional motion in an image sequence based on trajectories. Each gesture signal is represented as a time series in a Principal Component Analysis (PCA) reduced dimensional space. A class of Support Vector Machine (SVM) applicable to sequential-pattern recognition is employed for classification by incorporating a hybrid distance measure into the kernel function that accounts for both the hand shape and movement. The performance of the proposed method is evaluated in continuous tactile hand gesture streams recognition experiments. Results are presented for 9 different gestures performed by 25 subjects at a variety of time scales. Experimental results show that the proposed approach yields high recognition rate for hand gesture motion patterns.