Gesture recognition using quadratic curves

Authors:
Qiulei Dong;Yihong Wu;Zhanyi Hu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijng, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijng, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijng, P.R. China
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Year:
2006

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Abstract

This paper presents a novel method for human gesture recognition based on quadratic curves. Firstly, face and hands in the images are extracted by skin color and their central points are kept tracked by a modified Greedy Exchange algorithm. Then in each trajectory, the central points are fitted into a quadratic curve and 6 invariants from this quadratic curve are computed. Following these computations, a gesture feature vector composed of 6n such invariants is constructed, where n is the number of the trajectories in this gesture. Lastly, the gesture models are learnt from the feature vectors of gesture samples and an input gesture is recognized by comparing its feature vector with those of gesture models. In this gesture recognition method, the computational cost is low because the gesture duration does not need to be considered and only simple curvilinear integral and matrix computation are involved. Experiments on hip-hop dance show that our method can achieve a recognition rate as high as 97.65% on a database of 16 different gestures, each performed by 8 different people for 8 different times.