Comparing key frame selection for one-two hand gesture recognition using different methods

Abstract

Hand gesture recognition system has gained potential importance in application areas of human-computer interaction, machine vision, etc. Vision-based hand gesture recognition involves visual analysis of hand shape, position and/or motion. Finite State Machine (FSM) and Dynamic Time Warping (DTW) are most prominent methods used for gesture recognition. In this paper, we use object-based key frame selection from video sequence for segmenting Video Object Plane (VOP). Each VOP is a meaningful hand position, where Hausdorff and Euclidean distance are used for shape similarity. The key VOPs are selected on the basis of hand shape changes significantly and subsequently. The core approach of proposed work is that the frames of gesture video sequence are used with FSM and non-linear time alignment methods with key frame selection facility and trajectory features. Experimental results show accuracy and effectiveness of proposed system, for one-handed American and two-handed British sign language gesture recognition.