Robust Biometric Recognition From Palm Depth Images for Gloved Hands

Biometric recognition can be used to improve gesture-based interfaces by automatically identifying operators. Traditional palm biometric recognition techniques depend on palm appearance features, but these features are not available in an operating theater where gloves are worn. We propose a depth-based solution for palm biometric recognition. Based on the depth image, our system automatically segments the user's palm and extracts finger dimensions. The finger dimensions are further scaled according to the sensed depth to obtain the true finger dimensions, which are then used as features to characterize the palm. Finally, a modified k-nearest neighbors algorithm that assigns class labels based on the centroid displacement of each class in the neighboring points is applied to recognize the palm based on the geometric features. An accuracy of 96.24% was achieved for the biometric recognition of 4057 gloved palm samples captured at different angles and depths from 27 users. This accuracy is comparable with those of other state-of-the-art classification algorithms and demonstrates that biometric recognition may be viable for settings with gloved hands such as surgery.