HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps

Sensors

Diego G. Santos²  Bruno J. T. Fernandes¹  Byron L. D. Bezerra² 

[1] Escola Politécnica de Pernambuco, Universidade de Pernambuco, R. Benfica, 455-Madalena, Recife-PE 50720-001, Brazil;

关键词: HCI;    dynamic gesture;    HMM;    DTW;    CIPBR;   

DOI  :  10.3390/s151128646

来源: mdpi

【 摘 要 】

The hand is an important part of the body used to express information through gestures, and its movements can be used in dynamic gesture recognition systems based on computer vision with practical applications, such as medical, games and sign language. Although depth sensors have led to great progress in gesture recognition, hand gesture recognition still is an open problem because of its complexity, which is due to the large number of small articulations in a hand. This paper proposes a novel approach for hand gesture recognition with depth maps generated by the Microsoft Kinect Sensor (Microsoft, Redmond, WA, USA) using a variation of the CIPBR (convex invariant position based on RANSAC) algorithm and a hybrid classifier composed of dynamic time warping (DTW) and Hidden Markov models (HMM), called the hybrid approach for gesture recognition with depth maps (HAGR-D). The experiments show that the proposed model overcomes other algorithms presented in the literature in hand gesture recognition tasks, achieving a classification rate of 97.49% in the MSRGesture3D dataset and 98.43% in the RPPDI dynamic gesture dataset.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.