Hand motion recognition via fuzzy active curve axis Gaussian mixture models: A comparative study

Unconstrained human hand motions consisting grasp motion and in-hand manipulation lead to a fundamental challenge that many algorithms have to face in both theoretical and practical development, mainly due to the complexity and dexterity of the human hand. In this paper, fuzzy active curve axis Gaussian Mixture Model (FAcaGMM) is proposed by introducing a weighting exponent on the fuzzy membership into active curve axis Gaussian Mixture Models (AcaGMM) to improve its convergence efficiency, and then FAcaGMM is used to recognize human hand motions. In addition, a comparative study of recognition methods including FAcaGMM, Time Clustering (TC), Empirical Copula (EC), GMM and HMM is presented to recognize human hand motions including both grasps and in-hand manipulations from different subjects with varying training samples.