Similarity Measure Functions for Strategy-Based Biometrics
Functioning of a biometric system in large part
depends on the performance of the similarity measure function.
Frequently a generalized similarity distance measure function such as
Euclidian distance or Mahalanobis distance is applied to the task of
matching biometric feature vectors. However, often accuracy of a
biometric system can be greatly improved by designing a customized
matching algorithm optimized for a particular biometric application.
In this paper we propose a tailored similarity measure function for
behavioral biometric systems based on the expert knowledge of the
feature level data in the domain. We compare performance of a
proposed matching algorithm to that of other well known similarity
distance functions and demonstrate its superiority with respect to the
chosen domain.
[1] M. Badizadegan, Texas Hold'em Flop Types, Goldstar Books, Los
Angeles, California, 1999.
[2] P. M. Baggenstoss, Class-specific classifier: avoiding the curse of
dimensionality, Aerospace and Electronic Systems Magazine, Jan 2004,
pp. 37- 52.
[3] C. Barral, J. Coron and D. Naccache, Externalized Fingerprint
Matching, Cryptology ePrint Archive, 2004.
[4] H. Eidenberger, Evaluation and Analysis of Similarity Measures for
Content-based Visual Information Retrieval, ACM Multimedia Systems
Journal, 2006.
[5] A. K. Jain, R. Bolle and S. Pankanti, BIOMETRICS: Personal
Identification in Networked Society, Kluwer Academic Publishers, 1999.
[6] T. Kinnunen and I. ainen, Class-discriminative weighted distortion
measure for VQ-based speaker identification, In Proc. Joint IAPR
International Workshop on Statistical Pattern Recognition, Windsor,
Canada, August 6-9, 2002, pp. 681-688.
[7] K. Lee and H. Park, A New Similarity Measure Based on Intraclass
Statistics for Biometric Systems, ETRI Journal, Oct. 2003, pp. 401-406.
[8] H. Lei, S. Palla and V. Govindaraju, ER2: An Intuitive Similarity
Measure for On-Line Signature Verification, IWFHR '04: Proceedings
of the Ninth International Workshop on Frontiers in Handwriting
Recognition (IWFHR'04), IEEE Computer Society, 2004, pp. 191--195.
[9] O. Mut and M. Gökt├╝rk, Improved Weighted Matching for Speaker
Recognition, The Third World Enformatika Conference, WEC'05,
Istanbul, Turkey, April 27-29, 2005, pp. 229-231.
[10] M. Neuhaus and H. Bunke, An error-tolerant approximate matching
algorithm for attributed planar graphs and its application to fingerprint
classification, Proc. Joint IAPR Int. Workshops Structural, Syntactic,
and Statistical Pattern Recognition, 2004, pp. 180 -189.
[11] Poker-edge.com, Stats and Analysis, Available at: http://www.pokeredge.
com/stats.php, Retrieved June 7, 2006.
[12] A. Schwaighofer, Sorting it out: Machine learning and fingerprints,
Telematik, 2002, pp. 18-20.
[13] A. Sturn, Cluster Analysis for Large Scale Gene Expression Studies,
Masters Thesis. The Institute for Genomic Research, Rockville,
Maryland, USA, December 20, 2000.
[14] Wikipedia, Mahalanobis Distance, Available at:
http://en.wikipedia.org/wiki/Mahalanobis_distance, Retrieved August
22, 2006.
[15] R. V. Yampolskiy, Behavior Based Identification of Network Intruders,
19th Annual CSE Graduate Conference (Grad-Conf2006), Buffalo, NY,
February 24, 2006.
[16] R. V. Yampolskiy and V. Govindaraju, Use of Behavioral Biometrics in
Intrusion Detection and Online Gaming, Biometric Technology for
Human Identification III. SPIE Defense and Security Symposium,
Orlando, Florida, 17-22 April 2006.
[17] S. Yang and I. Verbauwhede, A Secure Fingerprint Matching
Technique, In Proc. ACM Workshop on Biometrics Methods and
Applications, 2003, pp. 89-94.
[1] M. Badizadegan, Texas Hold'em Flop Types, Goldstar Books, Los
Angeles, California, 1999.
[2] P. M. Baggenstoss, Class-specific classifier: avoiding the curse of
dimensionality, Aerospace and Electronic Systems Magazine, Jan 2004,
pp. 37- 52.
[3] C. Barral, J. Coron and D. Naccache, Externalized Fingerprint
Matching, Cryptology ePrint Archive, 2004.
[4] H. Eidenberger, Evaluation and Analysis of Similarity Measures for
Content-based Visual Information Retrieval, ACM Multimedia Systems
Journal, 2006.
[5] A. K. Jain, R. Bolle and S. Pankanti, BIOMETRICS: Personal
Identification in Networked Society, Kluwer Academic Publishers, 1999.
[6] T. Kinnunen and I. ainen, Class-discriminative weighted distortion
measure for VQ-based speaker identification, In Proc. Joint IAPR
International Workshop on Statistical Pattern Recognition, Windsor,
Canada, August 6-9, 2002, pp. 681-688.
[7] K. Lee and H. Park, A New Similarity Measure Based on Intraclass
Statistics for Biometric Systems, ETRI Journal, Oct. 2003, pp. 401-406.
[8] H. Lei, S. Palla and V. Govindaraju, ER2: An Intuitive Similarity
Measure for On-Line Signature Verification, IWFHR '04: Proceedings
of the Ninth International Workshop on Frontiers in Handwriting
Recognition (IWFHR'04), IEEE Computer Society, 2004, pp. 191--195.
[9] O. Mut and M. Gökt├╝rk, Improved Weighted Matching for Speaker
Recognition, The Third World Enformatika Conference, WEC'05,
Istanbul, Turkey, April 27-29, 2005, pp. 229-231.
[10] M. Neuhaus and H. Bunke, An error-tolerant approximate matching
algorithm for attributed planar graphs and its application to fingerprint
classification, Proc. Joint IAPR Int. Workshops Structural, Syntactic,
and Statistical Pattern Recognition, 2004, pp. 180 -189.
[11] Poker-edge.com, Stats and Analysis, Available at: http://www.pokeredge.
com/stats.php, Retrieved June 7, 2006.
[12] A. Schwaighofer, Sorting it out: Machine learning and fingerprints,
Telematik, 2002, pp. 18-20.
[13] A. Sturn, Cluster Analysis for Large Scale Gene Expression Studies,
Masters Thesis. The Institute for Genomic Research, Rockville,
Maryland, USA, December 20, 2000.
[14] Wikipedia, Mahalanobis Distance, Available at:
http://en.wikipedia.org/wiki/Mahalanobis_distance, Retrieved August
22, 2006.
[15] R. V. Yampolskiy, Behavior Based Identification of Network Intruders,
19th Annual CSE Graduate Conference (Grad-Conf2006), Buffalo, NY,
February 24, 2006.
[16] R. V. Yampolskiy and V. Govindaraju, Use of Behavioral Biometrics in
Intrusion Detection and Online Gaming, Biometric Technology for
Human Identification III. SPIE Defense and Security Symposium,
Orlando, Florida, 17-22 April 2006.
[17] S. Yang and I. Verbauwhede, A Secure Fingerprint Matching
Technique, In Proc. ACM Workshop on Biometrics Methods and
Applications, 2003, pp. 89-94.
@article{"International Journal of Information, Control and Computer Sciences:59915", author = "Roman V. Yampolskiy and Venu Govindaraju", title = "Similarity Measure Functions for Strategy-Based Biometrics", abstract = "Functioning of a biometric system in large part
depends on the performance of the similarity measure function.
Frequently a generalized similarity distance measure function such as
Euclidian distance or Mahalanobis distance is applied to the task of
matching biometric feature vectors. However, often accuracy of a
biometric system can be greatly improved by designing a customized
matching algorithm optimized for a particular biometric application.
In this paper we propose a tailored similarity measure function for
behavioral biometric systems based on the expert knowledge of the
feature level data in the domain. We compare performance of a
proposed matching algorithm to that of other well known similarity
distance functions and demonstrate its superiority with respect to the
chosen domain.", keywords = "Behavioral Biometrics, Euclidian Distance,Matching, Similarity Measure.", volume = "2", number = "12", pages = "4172-6", }
