An Efficient Hand Dorsal Vein Recognition Based on Neural Networks

This paper proposes an effective human hand vascular pattern recognition system by using multi-layered perceptron neural networks for biometric identification applications. Biometric hand dorsal vein images are acquired by using NIR (near infra-red) illuminated CCD camera from 103 persons of different ages and gender. The vascular region of interest (ROI) is cropped from hand images firstly and then mean filter and histogram equalization processes were applied to the 240x180 pixels resolution hand vein pattern images in order to restrain the noises. The gray-scaled vein pattern images were converted to the binary format by applying Otsu’s thresholding method. The resulting images then are divided into 20x20 pixel dimensioned sub-images before feature extraction. Average absolute deviation (AAD) algorithm was implemented to these sub-images for getting the feature sets. Multi-layer perceptron neural network (MLPNN) method was performed for identification of the human hand vein pattern images. Experimental results showed that the proposed method achieved correct classification rates up to 100%

[1]  G. Sahoo,et al.  Comparison of Neural Network Training Algorithms for the prediction of the patient's post-operative recovery area , 2009, J. Convergence Inf. Technol..

[2]  Sangkyun Im,et al.  A filter bank algorithm for hand vascular pattern biometrics , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[3]  Kejun Wang,et al.  A study of hand vein recognition method , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[4]  Naushad Mamode Khan,et al.  Feature Extraction of Dorsal Hand Vein Pattern Using a Fast Modified PCA Algorithm Based On Cholesky Decomposition and Lanczos Technique , 2010 .

[5]  Ching-Yuen Chan,et al.  A Real Time Quality Monitoring System for the Lighting Industry: A Practical and Rapid Approach Using Computer Vision and Image Processing (CVIP) Tools , 2011 .

[6]  Roger Boyle.  Computer vision , 1988 .

[7]  R. C. Thomas,et al.  Computer Vision: A First Course , 1988 .

[8]  Scott E. Umbaugh,et al.  Computer Vision and Image Processing: A Practical Approach Using CVIPTools , 1997 .

[9]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[10]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[11]  Xiao Han,et al.  Feature Extraction of Hand-Vein Patterns Based on Ridgelet Transform and Local Interconnection Structure Neural Network , 2006 .

[12]  Lingyu Wang,et al.  A Thermal Hand Vein Pattern Verification System , 2005, ICAPR.

[13]  Moulay A. Akhloufi,et al.  Using lock-in infrared thermography for the visualization of the hand vascular tree , 2008, SPIE Defense + Commercial Sensing.

[14]  Ahmed M. Badawi.  Hand Vein Biometric Verification Prototype: A Testing Performance and Patterns Similarity , 2006, IPCV.

[15]  G. Leedham,et al.  Infrared imaging of hand vein patterns for biometric purposes , 2007 .

[16]  Brian C. Lovell,et al.  Biometric Authentication Based on Infrared Thermal Hand Vein Patterns , 2009, 2009 Digital Image Computing: Techniques and Applications.

[17]  Soo-Won Kim,et al.  An biometric identification system by extracting hand vein patterns , 2001 .

[18]  Ahmed M. Badawi,et al.  Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns , 2008 .

[19]  Soo-Won Kim,et al.  A Direction-Based Vascular Pattern Extraction Algorithm for Hand Vascular Pattern Verification , 2003 .