The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - May (2011 vol.33)
pp: 945-957
Jifeng Dai , Tsinghua University, Beijing
ABSTRACT
Palmprint is a promising biometric feature for use in access control and forensic applications. Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e.g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted. In this paper, we propose a novel recognition algorithm for high-resolution palmprint. The main contributions of the proposed algorithm include the following: 1) use of multiple features, namely, minutiae, density, orientation, and principal lines, for palmprint recognition to significantly improve the matching performance of the conventional algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the existing algorithm in case of regions with a large number of creases. 3) Use of a novel fusion scheme for an identification application which performs better than conventional fusion methods, e.g., weighted sum rule, SVMs, or Neyman-Pearson rule. Besides, we analyze the discriminative power of different feature combinations and find that density is very useful for palmprint recognition. Experimental results on the database containing 14,576 full palmprints show that the proposed algorithm has achieved a good performance. In the case of verification, the recognition system's False Rejection Rate (FRR) is 16 percent, which is 17 percent lower than the best existing algorithm at a False Acceptance Rate (FAR) of 10^{-5}, while in the identification experiment, the rank-1 live-scan partial palmprint recognition rate is improved from 82.0 to 91.7 percent.
INDEX TERMS
Palmprint, orientation field, the composite algorithm, density map, data fusion.
CITATION
Jifeng Dai, "Multifeature-Based High-Resolution Palmprint Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 5, pp. 945-957, May 2011, doi:10.1109/TPAMI.2010.164
REFERENCES
[1] A. Jain, P. Flynn, and A. Ross, Handbook of Biometrics. Springer, 2007.
[2] S. Dewan and W. Elementary, “Scan a Palm, Find a Clue,” The New York Times, 2003.
[3] The FBIs Next Generation Identification(NGI), http://fingerprint. nist.gov/standard/Presentations/ archivesNGI_Overview_ Feb_2005.pdf , 2009.
[4] F. Galton, Fingerprints. Wm. S. Hein Publishing, 2002.
[5] J. Zhou, F. Chen, N. Wu, and C. Wu, “Crease Detection from Fingerprint Images and Its Applications in Elderly People,” Pattern Recognition, vol. 42, no. 5, pp. 896-906, 2009.
[6] D. Huang, W. Jia, and D. Zhang, “Palmprint Verification Based on Principal Lines,” Pattern Recognition, vol. 41, no. 4, pp. 1316-1328, 2008.
[7] D. Ashbaugh, Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press, 1999.
[8] A. Jain and J. Feng, “Latent Palmprint Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1032-1047, July 2009.
[9] D. Zhang, W. Kong, J. You, and M. Wong, “Online Palmprint Identification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041-1050, Sept. 2003.
[10] W. Kong, D. Zhang, and W. Li, “Palmprint Feature Extraction Using 2-D Gabor Filters,” Pattern Recognition, vol. 36, no. 10, pp. 2339-2347, 2003.
[11] A. Kong and D. Zhang, “Competitive Coding Scheme for Palmprint Verification,” Proc. 17th Int'l Conf. Pattern Recognition, vol. 1, 2004.
[12] A. Kong, D. Zhang, and M. Kamel, “Palmprint Identification Using Feature-Level Fusion,” Pattern Recognition, vol. 39, no. 3, pp. 478-487, 2006.
[13] A. Kumar and D. Zhang, “Personal Recognition Using Hand and Texture,” IEEE Trans. Image Processing, vol. 15, no. 8, pp. 2454-2461, Aug. 2006.
[14] W. Jia, D. Huang, and D. Zhang, “Palmprint Verification Based on Robust Line Orientation Code,” Pattern Recognition, vol. 41, no. 5, pp. 1521-1530, 2008.
[15] W. Shu and D. Zhang, “Automated Personal Identification by Palmprint,” Optical Eng., vol. 37, no. 8, pp. 2359-2362, 1998.
[16] D. Zhang and W. Shu, “Two Novel Characteristics in Palmprint Verification: Datum Point Invariance and Line Feature Matching,” Pattern Recognition, vol. 32, no. 4, pp. 691-702, 1999.
[17] N. Duta, A. Jain, and K. Mardia, “Matching of Palmprints,” Pattern Recognition Letters, vol. 23, no. 4, pp. 477-486, 2002.
[18] J. You, W. Li, and D. Zhang, “Hierarchical Palmprint Identification via Multiple Feature Extraction,” Pattern Recognition, vol. 35, no. 4, pp. 847-859, 2002.
[19] C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Proc. Alvey Vision Conf., vol. 15, p. 50, 1988.
[20] Z. Sun, T. Tan, Y. Wang, and S. Li, “Ordinal Palmprint Represention for Personal Identification,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 279-284,
[21] F. Yue, W. Zuo, D. Zhang, and K. Wang, “Orientation Selection Using Modified FCM for Competitive-Based Palmprint Recognition,” Pattern Recognition, vol. 42, no. 11, pp. 2841-2849, 2009.
[22] A. Kumar, “Incorporating Cohort Information for Reliable Palmprint Authentication,” Proc. Sixth Indian Conf. Computer Vision, Graphics and Image Processing, pp. 583-590, 2008.
[23] J. Travis, “Forensic Friction Ridge (Fingerprint) Examination Validation Studies,” 2000.
[24] A. Jain, L. Hong, and R. Bolle, “On-Line Fingerprint Verification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 302-314, Apr. 1997.
[25] A. Jain, S. Prabhakar, and L. Hong, “A Multichannel Approach to Fingerprint Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 348-359, Apr. 1999.
[26] J. Zhou and J. Gu, “A Model-Based Method for the Computation of Fingerprints' Orientation Field,” IEEE Trans. Image Processing, vol. 13, no. 6, pp. 821-835, June 2004.
[27] J. Radon, “Uber die Bestimmung von Funktionen durch Ihre Integralwerte Langs Gewisser Mannigfaltigkeiten,” Berichte Sachsische Akademie der Wissenschaften, vol. 29, pp. 262-277, 1917.
[28] D. Wan and J. Zhou, “Fingerprint Recognition Using Model-Based Density Map,” IEEE Trans. Image Processing, vol. 15, no. 6, pp. 1690-1696, June 2006.
[29] N. Ratha, K. Karu, S. Chen, and A. Jain, “A Real-Time Matching System for Large Fingerprint Databases,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 799-813, Aug. 1996.
[30] D. Maltoni, A. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition. Springer, 2009.
[31] D. Ballard, “Generalizing the Hough Transform to Detect Arbitrary Shapes,” Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981.
[32] J. Wang, W. Yau, A. Suwandy, and E. Sung, “Person Recognition by Fusing Palmprint and Palm Vein Images Based on Laplacianpalm Representation,” Pattern Recognition, vol. 41, no. 5, pp. 1531-1544, 2008.
[33] A. Kumar and D. Zhang, “Personal Authentication Using Multiple Palmprint Representation,” Pattern Recognition, vol. 38, no. 10, pp. 1695-1704, 2005.
[34] P. Hennings-Yeomans, B. Kumar, and M. Savvides, “Palmprint Classification Using Multiple Advanced Correlation Filters and Palm-Specific Segmentation,” IEEE Trans. Information Forensics and Security, vol. 2, no. 3, pp. 613-622, Sept. 2007.
[35] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[36] S. Prabhakar and A. Jain, “Decision-Level Fusion in Fingerprint Verification,” Pattern Recognition, vol. 35, no. 4, pp. 861-874, 2002.
[37] J. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999.
[38] R. Bolle, J. Connell, S. Pankanti, N. Ratha, and A. Senior, “The Relation between the ROC Curve and the CMC,” Proc. Fourth IEEE Workshop Automatic Identification Advanced Technologies, pp. 15-20, 2005.
[39] J. Kitler, M. Hatef, R. Duin, and J. Matas, “On Combining Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[40] F. Chen, J. Zhou, and C. Yang, “Reconstructing Orientation Field from Fingerprint Minutiae to Improve Minutiae-Matching Accuracy.” IEEE Trans. Image Processing, vol. 18, no. 7, pp. 1665-1670, July 2009.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool