Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns
This paper presents a hand vein authentication system
using fast spatial correlation of hand vein patterns. In order to
evaluate the system performance, a prototype was designed and a
dataset of 50 persons of different ages above 16 and of different
gender, each has 10 images per person was acquired at different
intervals, 5 images for left hand and 5 images for right hand. In
verification testing analysis, we used 3 images to represent the
templates and 2 images for testing. Each of the 2 images is matched
with the existing 3 templates. FAR of 0.02% and FRR of 3.00 %
were reported at threshold 80. The system efficiency at this threshold
was found to be 99.95%. The system can operate at a 97% genuine
acceptance rate and 99.98 % genuine reject rate, at corresponding
threshold of 80. The EER was reported as 0.25 % at threshold 77. We
verified that no similarity exists between right and left hand vein
patterns for the same person over the acquired dataset sample.
Finally, this distinct 100 hand vein patterns dataset sample can be
accessed by researchers and students upon request for testing other
methods of hand veins matching.
[1] J. M. Cross and C. L. Smith, "Thermographic Imaging of the
Subcutaneous Vascular Network of the Back of the Hand for Biometric
Identification", Proceedings of 29th International Carnahan Conference
on Security Technology, Institute of Electrical and Electronics
Engineers, pp. 20-35, 1995.
[2] S. Im, H. Park, Y. Kim, S. Han, S. Kim, C. Kang, and C. Chung, "A
Biometric Identification System by Extracting Hand Vein Patterns",
Journal of the Korean Physical Society, vol. 38-3, pp. 268-272, March
2001.
[3] T. Tanaka and N. Kubo, "Biometric Authentication by Hand Vein
Patterns" SICE, Annual Conference in Sapporo, pp. 249-253, Aug. 2004.
[4] N. Miura, A. Nagasaka, and T. Miyatake, "Feature Extraction of Finger-
Vein Patterns Based on Repeated Line Tracking and its Application to
Personal Identification", Machine Vision and Applications, vol. 15, pp.
194-203, 2004.
[5] C. Lin and K. Fan, "Biometric Verification Using Thermal Images of
Palm-Dorsa Vein Patterns", IEEE Transactions on Circuits and systems
for Video Technology vol. 14, No. 2, February 2004.
[6] O. Such, "Near Infrared Imaging of Hemodynamics", Proceedings of
18th International Conference of the IEEE/EMBS, Amsterdam, Nov.
1996.
[7] O. Such, Sabine Acker, and Valdimir Blazek, "Mapped Hemodynamic
Data Acquisition by Near Infrared CCD Imaging", Proceedings of 19th
International Conference of the IEEE/EMBS, Chicago, Oct. 1997.
[8] E. K. Y. Chan and J. A. Pearce, "A Rule-Based, Adaptive Window-Size
Filter for the Enhancement of Subcutaneous Vascular Patterns in
Thermographic Images", Proceedings of 11th International Conference
of the IEEE/EMBS, pp. 1746-1748, Oct. 1989.
[9] K. Eric, Y. Chan and J. Pearce, "Visualization of Dynamic Subcutaneous
Vasomotor Response by Computer-Assisted Thermography", IEEE
Transactions on Biomedical Engineering, vol. 37-8, pp. 786-795,
August 1990.
[10] K. Eric, Y. Chan and J. Pearce, "A Computer-Assisted Thermography
System for the Extraction, Visualization and Tracing of Subcutaneous
Peripheral Venous Patterns", IEEE International Symposium on Circuits
and Systems, vol. 1, pp. 508 -511, 1991.
[11] Y. Sun, "Automatic Identification of Vessel Contours in Coronary
Arteries in Arteriograms by an Adaptive Tracing Algorithm", IEEE
Transactions on Medical Imaging, vol. 8-1, pp. 78-88, March 1989.
[12] F. Van Der Heijden, Image Based Measurement Systems, John Wiley &
Sons, 1995.
[13] M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and
Machine Vision, Chapman & Hall, 1993.
[14] R. Schalkoff, Pattern Recognition: Statistical, Structural and Neural
Approaches, John Wiley & Sons, 1992.
[15] A. Papolis, Probability, Random Variables and Stochastic Processes,
Third Edition, McGraw-Hill, New York, 1991.
[16] K. Abd-Elmoniem, A. Youssef, and Y. Kadah, "Real-Time Speckle
Reduction and Coherence Enhancement in Ultrasound Imaging via
Nonlinear Anisotropic Diffusion", IEEE Transactions on Biomedical
Engineering, vol. 49-9, pp. 997-1014, 2002.
[17] Ahmed M. Badawi, Muhammed A. Rushdi, "Speckle Reduction in
Medical Ultrasound: A Novel Scatterer Density Weighted Nonlinear
Diffusion Algorithm Implemented as Neural Network Filter,"
Proceedings of the 28th IEEE 2006 International Conference of the
Engineering in Medicine and Biology Society (IEEE/EMBS), pp. 2776-
2782, Aug 30-Sep 3, New York City, USA, 2006.
[18] S. Pankanti, S. Prabhakar, and A. Jain, "On the Individuality of
Fingerprints", IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 24-8, pp. 1010-1025, Aug. 2002.
[19] A. Jain, S. Prabhakar, L. Hong, and S. Pankanti, "Filterbank-Based
Fingerprint Matching", IEEE Transactions on Image Processing, vol. 9-
5, pp. 846-859, 2000.
[20] A. Jain, L. Hong, and R. Bolle, "On-Line Fingerprint Verification",
IEEE Transactions on Pattern Anakysis and Machine Intelligence, vol.
19-4, pp. 302-314, 1997.
[21] R. Sanchez-Reillo, C. Sanchez-Avilla, and A. Gonzalez-Macros,
"Biometrics Identification Through Hand Geometry Measurements",
IEEE Transactions on Pattern Anakysis and Machine Intelligence, vol.
22-18, pp. 1168-1171, 2000.
[22] L. Wang, G. Leedham, "Near- and Far- Infrared Imaging for Vein
Pattern Biometrics", IEEE International Conference on Video and
Signal Based Surveillance (AVSS'06), 2006.
[1] J. M. Cross and C. L. Smith, "Thermographic Imaging of the
Subcutaneous Vascular Network of the Back of the Hand for Biometric
Identification", Proceedings of 29th International Carnahan Conference
on Security Technology, Institute of Electrical and Electronics
Engineers, pp. 20-35, 1995.
[2] S. Im, H. Park, Y. Kim, S. Han, S. Kim, C. Kang, and C. Chung, "A
Biometric Identification System by Extracting Hand Vein Patterns",
Journal of the Korean Physical Society, vol. 38-3, pp. 268-272, March
2001.
[3] T. Tanaka and N. Kubo, "Biometric Authentication by Hand Vein
Patterns" SICE, Annual Conference in Sapporo, pp. 249-253, Aug. 2004.
[4] N. Miura, A. Nagasaka, and T. Miyatake, "Feature Extraction of Finger-
Vein Patterns Based on Repeated Line Tracking and its Application to
Personal Identification", Machine Vision and Applications, vol. 15, pp.
194-203, 2004.
[5] C. Lin and K. Fan, "Biometric Verification Using Thermal Images of
Palm-Dorsa Vein Patterns", IEEE Transactions on Circuits and systems
for Video Technology vol. 14, No. 2, February 2004.
[6] O. Such, "Near Infrared Imaging of Hemodynamics", Proceedings of
18th International Conference of the IEEE/EMBS, Amsterdam, Nov.
1996.
[7] O. Such, Sabine Acker, and Valdimir Blazek, "Mapped Hemodynamic
Data Acquisition by Near Infrared CCD Imaging", Proceedings of 19th
International Conference of the IEEE/EMBS, Chicago, Oct. 1997.
[8] E. K. Y. Chan and J. A. Pearce, "A Rule-Based, Adaptive Window-Size
Filter for the Enhancement of Subcutaneous Vascular Patterns in
Thermographic Images", Proceedings of 11th International Conference
of the IEEE/EMBS, pp. 1746-1748, Oct. 1989.
[9] K. Eric, Y. Chan and J. Pearce, "Visualization of Dynamic Subcutaneous
Vasomotor Response by Computer-Assisted Thermography", IEEE
Transactions on Biomedical Engineering, vol. 37-8, pp. 786-795,
August 1990.
[10] K. Eric, Y. Chan and J. Pearce, "A Computer-Assisted Thermography
System for the Extraction, Visualization and Tracing of Subcutaneous
Peripheral Venous Patterns", IEEE International Symposium on Circuits
and Systems, vol. 1, pp. 508 -511, 1991.
[11] Y. Sun, "Automatic Identification of Vessel Contours in Coronary
Arteries in Arteriograms by an Adaptive Tracing Algorithm", IEEE
Transactions on Medical Imaging, vol. 8-1, pp. 78-88, March 1989.
[12] F. Van Der Heijden, Image Based Measurement Systems, John Wiley &
Sons, 1995.
[13] M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and
Machine Vision, Chapman & Hall, 1993.
[14] R. Schalkoff, Pattern Recognition: Statistical, Structural and Neural
Approaches, John Wiley & Sons, 1992.
[15] A. Papolis, Probability, Random Variables and Stochastic Processes,
Third Edition, McGraw-Hill, New York, 1991.
[16] K. Abd-Elmoniem, A. Youssef, and Y. Kadah, "Real-Time Speckle
Reduction and Coherence Enhancement in Ultrasound Imaging via
Nonlinear Anisotropic Diffusion", IEEE Transactions on Biomedical
Engineering, vol. 49-9, pp. 997-1014, 2002.
[17] Ahmed M. Badawi, Muhammed A. Rushdi, "Speckle Reduction in
Medical Ultrasound: A Novel Scatterer Density Weighted Nonlinear
Diffusion Algorithm Implemented as Neural Network Filter,"
Proceedings of the 28th IEEE 2006 International Conference of the
Engineering in Medicine and Biology Society (IEEE/EMBS), pp. 2776-
2782, Aug 30-Sep 3, New York City, USA, 2006.
[18] S. Pankanti, S. Prabhakar, and A. Jain, "On the Individuality of
Fingerprints", IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 24-8, pp. 1010-1025, Aug. 2002.
[19] A. Jain, S. Prabhakar, L. Hong, and S. Pankanti, "Filterbank-Based
Fingerprint Matching", IEEE Transactions on Image Processing, vol. 9-
5, pp. 846-859, 2000.
[20] A. Jain, L. Hong, and R. Bolle, "On-Line Fingerprint Verification",
IEEE Transactions on Pattern Anakysis and Machine Intelligence, vol.
19-4, pp. 302-314, 1997.
[21] R. Sanchez-Reillo, C. Sanchez-Avilla, and A. Gonzalez-Macros,
"Biometrics Identification Through Hand Geometry Measurements",
IEEE Transactions on Pattern Anakysis and Machine Intelligence, vol.
22-18, pp. 1168-1171, 2000.
[22] L. Wang, G. Leedham, "Near- and Far- Infrared Imaging for Vein
Pattern Biometrics", IEEE International Conference on Video and
Signal Based Surveillance (AVSS'06), 2006.
@article{"International Journal of Biological, Life and Agricultural Sciences:56459", author = "Mohamed Shahin and Ahmed Badawi and Mohamed Kamel", title = "Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns", abstract = "This paper presents a hand vein authentication system
using fast spatial correlation of hand vein patterns. In order to
evaluate the system performance, a prototype was designed and a
dataset of 50 persons of different ages above 16 and of different
gender, each has 10 images per person was acquired at different
intervals, 5 images for left hand and 5 images for right hand. In
verification testing analysis, we used 3 images to represent the
templates and 2 images for testing. Each of the 2 images is matched
with the existing 3 templates. FAR of 0.02% and FRR of 3.00 %
were reported at threshold 80. The system efficiency at this threshold
was found to be 99.95%. The system can operate at a 97% genuine
acceptance rate and 99.98 % genuine reject rate, at corresponding
threshold of 80. The EER was reported as 0.25 % at threshold 77. We
verified that no similarity exists between right and left hand vein
patterns for the same person over the acquired dataset sample.
Finally, this distinct 100 hand vein patterns dataset sample can be
accessed by researchers and students upon request for testing other
methods of hand veins matching.", keywords = "Biometrics, Verification, Hand Veins, PatternsSimilarity, Statistical Performance.", volume = "2", number = "1", pages = "21-6", }
