Adaptive multiple sets of CSS features for hand posture recognition

Abstract In this paper, an adaptive feature extraction approach based on curvature scale space (CSS) is presented for translation, scale, and rotation invariant recognition of hand postures. First, hands are segmented from hand posture images into binary silhouettes and then binary hand contours are computed. CSS images are then used to represent the contours of hand postures. In particular, adaptive multiple sets of CSS features are extracted to address the problem of deep concavities in the contours of hand postures. Finally, 1-nearest neighbor techniques are used to perform adaptive multiple sets of CSS feature matching for hand posture identification. Results indicate that the proposed approach performs well in the recognition of hand postures. And, the proposed approach is more accurate than previous methods which were based on conventional features. The proposed technique could be useful in improving the recognition of hand postures.

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