Image set-based hand shape recognition using camera selection driven by multi-class AdaBoosting

Authors:
Yasuhiro Ohkawa;Chendra Hadi Suryanto;Kazuhiro Fukui
Affiliations:
Kazuhiro Fukui Graduate School of Systems and Information Engineering, University of Tsukuba, Japan;Kazuhiro Fukui Graduate School of Systems and Information Engineering, University of Tsukuba, Japan;Kazuhiro Fukui Graduate School of Systems and Information Engineering, University of Tsukuba, Japan
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Year:
2011

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Abstract

We propose a method for image set-based hand shape recognition that uses the multi-class AdaBoost framework. The recognition of hand shape is a difficult problem, as a hand's appearance depends greatly on view point and individual characteristics. Using multiple images from a video camera or a multiple-camera system is known to be an effective solution to this problem. In our proposed method, a simple linear mutual subspace method is considered as a weak classifier. Finally, strong classifiers are constructed by integrating the weak classifiers. The effectiveness of the proposed method is demonstrated through experiments using a dataset of 27 types of hand shapes. Our method achieves comparable performance to the kernel orthogonal mutual subspace method, but at a smaller computational cost.