Enhancement of Accuracy of Hand Shape Recognition Using Color Calibration by Clustering Scheme and Majority Voting Method
This paper presents methods of enhancing the recognition accuracy of hand shapes in a scheme which is proposed by the authors as being easy to memorize and which can represent much information. To ensure suitability for practical use, the recognition performance must be maintained even when there are changes in the illumination environment. First, a color calibration process using a k-means clustering scheme is introduced as a way of ensuring high performance in color detection. In the proposed method the thresholds for hue values are decided before the recognition process, as a color calibration scheme. The second method of enhancing accuracy involves making a majority decision. Many image frames are obtained from one hand shape before the transition to the next shape. The frames in this hand shape formation time span are used for shape recognition by majority voting based on the recognition results from each frame. It has been verified by carrying out experiments under different illumination conditions that the proposed technique can raise the recognition performance.
KeywordsColor Gloves Shape Recognition Color Detection Hue Value Clustering Majority Voting Illumination Environment
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