Most Probable Longest Common Subsequence for Recognition of Gesture Character Input

This paper presents a technique for trajectory classification with applications to dynamic free-air hand gesture recognition. Such gestures are unencumbered and drawn in free air. Our approach is an extension to the longest common subsequence (LCS) classification algorithm. A learning preprocessing stage is performed to create a probabilistic 2-D template for each gesture, which allows taking into account different trajectory distortions with different probabilities. The modified LCS, termed the most probable LCS (MPLCS), is developed to measure the similarity between the probabilistic template and the hand gesture sample. The final decision is based on the length and probability of the extracted subsequence. Validation tests using a cohort of gesture digits from video-based capture show that the approach is promising with a recognition rate of more than 98 % for video stream preisolated digits. The MPLCS algorithm can be integrated into a gesture recognition interface to facilitate gesture character input. This can greatly enhance the usability of such interfaces.