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Novel Haar features for real-time hand gesture recognition using SVM

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Abstract

Due to the effect of lighting and complex background, most visual hand gesture recognition systems work only under restricted environments. Here, we propose a robust system which consists of three modules: digital zoom, adaptive skin detection, and hand gesture recognition. The first module detects user face and zooms in so that the face and upper torus take the central part of the image. The second module utilizes the detected user facial color information to detect the other skin color regions like hands. The last module is the most important part for doing both static and dynamic hand gesture recognition. The region of interest next to the detected user face is for fist/waving hand gesture recognition. To classify the dynamic hand gestures under complex background, motion history image and four groups of novel Haar-like features are investigated to classify the dynamic up, down, left, and right hand gestures. A simple efficient algorithm using Support Vector Machine is developed. These defined hand gestures are intuitive and easy for user to control most home appliances. Five users doing 50 dynamic hand gestures at near, medium, and far distances, respectively, were tested under complex environments. Experimental results showed that the accuracy was 95.37 % on average and the processing speed was 3.93 ms per frame. An application integrated with the developed hand gesture recognition was also given to demonstrate the feasibility of proposed system.

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Notes

  1. http://msdn.microsoft.com/zh-tw/hh367958.aspx.

  2. http://note.sonots.com/SciSoftware/haartraining.html.

  3. http://www.cooliris.com/.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Tatung University, No. 40, Sec. 3, Jhongshan N. Rd, Taipei, 104, Taiwan, R.O.C.

    Chen-Chiung Hsieh & Dung-Hua Liou

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  1. Chen-Chiung Hsieh
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  2. Dung-Hua Liou
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Correspondence to Chen-Chiung Hsieh.

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Hsieh, CC., Liou, DH. Novel Haar features for real-time hand gesture recognition using SVM. J Real-Time Image Proc 10, 357–370 (2015). https://doi.org/10.1007/s11554-012-0295-0

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