Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining implicit polynomials and geometric features for hand recognition
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Peg-Free Hand Shape Verification Using High Order Zernike Moments
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Personal authentication using hand images
Pattern Recognition Letters
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Hand geometry identification without feature extraction by general regression neural network
Expert Systems with Applications: An International Journal
Biometric Verification by Fusing Hand Geometry and Palmprint
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
Design and Implementation of a Robust Palm Biometrics Recognition and Verification System
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Biometric recognition by fusing palmprint and hand-geometry based on morphology
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Image and Vision Computing
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This paper presents an approach for personal identification using hand geometrical features, in which the infrared illumination device is employed to improve the usability of this hand recognition system. In the proposed system, prospective users can place their hand freely in front of the camera without any pegs or templates. Moreover, the proposed system can be widely used under dark environment and complex background scenarios. To achieve better detection accuracy, in total 13 important points are detected from a palm image, and 34 features calculated from these points are used to further recognition. Experimental results demonstrate that the averaged Correct Identification Rate (CIR) is 96.23% and averaged False Accept Rate (FAR) is 1.85%. These results prove that the proposed contact-free system can be considered as an effective identity verification system for practical applications.