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Abstract
There is a high demand for contactless biometrics due to social concern for more convenient and hygienic technology. However, contactless biometrics like palm vein recognition faces a number of challenges. Hand rotation is among the main challenges to be solved. A number of methods have been introduced to extract palm region of interest (ROI) and have reported high accuracy, but the maximum rotation that can be handled by the methods are never mentioned. Usually, the ROI detection method fails to work if a large rotation exists in the hand position. In this paper, we propose a rotation-invariant method that can detect a palm ROI in any rotation angles. The method can sustain the recognition accuracy despite a substantial hand rotation of up to 360°. ROI detection in different angles causes minor ROI dislocation, thus a feature extraction technique called Winner-Take-All hashing (WTA) is developed. WTA serves as a feature enhancer, and is equipped with the Fisher’s Discriminant criterion to obtain a discriminative representation from the palm ROI. Experiment result shows that the proposed method is able to achieve a maximum recall of 94% and an EER at 7.12% for a challenging scenario containing hand rotation of up to 360°.
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