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Robust and Efficient Ridge-Based Palmprint Matching
Aug. 2012 (vol. 34 no. 8)
pp. 1618-1632
Jianjiang Feng, Dept. of Autom., Tsinghua Univ., Beijing, China
Jifeng Dai, Dept. of Autom., Tsinghua Univ., Beijing, China
Jie Zhou, Dept. of Autom., Tsinghua Univ., Beijing, China
During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions. Recently, several ridge-based palmprint matching algorithms have been proposed to fill the gap. Major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching, while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints. However, palmprints differ from fingerprints in several aspects: 1) Palmprints are much larger and thus contain a large number of minutiae, 2) palms are more deformable than fingertips, and 3) the quality and discrimination power of different regions in palmprints vary significantly. As a result, these matchers are unable to appropriately handle the distortion and noise, despite heavy computational cost. Motivated by the matching strategies of human palmprint experts, we developed a novel palmprint recognition system. The main contributions are as follows: 1) Statistics of major features in palmprints are quantitatively studied, 2) a segment-based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions, and 3) to reduce the computational complexity, an orientation field-based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage. The proposed matcher is tested by matching 840 query palmprints against a gallery set of 13,736 palmprints. Experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed.

[1] D.R. Ashbaugh, Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press, 1999.
[2] M. Kücken and A. Newell, "Fingerprint Formation," J. Theoretical Biology, vol. 235, no. 1, pp. 71-83, 2005.
[3] H. Cummins and M. Midlo, Finger Prints, Palms and Soles: An Introduction to Dermatoglyphics. Dover Publications, 1961.
[4] W. Li, D. Zhang, and Z. Xu, "Palmprint Identification by Fourier Transform," Int'l J. Pattern Recognition and Artificial Intelligence, vol. 16, no. 4, pp. 417-432, 2002.
[5] D. Zhang, W.K. 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.
[6] 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.
[7] A. Kong and D. Zhang, "Competitive Coding Scheme for Palmprint Verification," Proc. 17th Int'l Conf. Pattern Recognition, vol. 1, 2004.
[8] A. Kong, D. Zhang, and M. Kamel, "Palmprint Identification Using Feature-Level Fusion," Pattern Recognition, vol. 39, no. 3, pp. 478-487, 2006.
[9] A. Kumar and D. Zhang, "Personal Recognition Using Hand Shape and Texture," IEEE Trans. Image Processing, vol. 15, no. 8, pp. 2454-2461, Aug. 2006.
[10] D. Huang, W. Jia, and D. Zhang, "Palmprint Verification Based on Principal Lines," Pattern Recognition, vol. 41, no. 4, pp. 1316-1328, 2008.
[11] 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.
[12] W. Shu and D. Zhang, "Automated Personal Identification by Palmprint," Optical Eng., vol. 37, no. 8, pp. 2359-2362, 1998.
[13] 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.
[14] N. Duta, A.K. Jain, and K. Mardia, "Matching of Palmprints," Pattern Recognition Letters, vol. 23, no. 4, pp. 477-486, 2002.
[15] J. You, W. Li, and D. Zhang, "Hierarchical Palmprint Identification via Multiple Feature Extraction," Pattern Recognition, vol. 35, no. 4, pp. 847-859, 2002.
[16] Z. Sun, T. Tan, Y. Wang, and S. Li, "Ordinal Palmprint Representation for Personal Identification," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 279-284, 2005.
[17] 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.
[18] A. Kumar, "Incorporating Cohort Information for Reliable Palmprint Authentication," Proc. Indian Conf. Computer Vision, Graphics and Image Processing, pp. 583-590, 2008.
[19] "Data Format for the Interchange of Fingerprint Facial, & Other Biometric Information," ANSI/NIST-ITL, 1-2007, http://www. . 2012.
[20] A.K. Jain and J. Feng, "Latent Palmprint Matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 6, pp. 1032-1047, June 2009.
[21] J. Dai and J. Zhou, "Multifeature-Based High-Resolution Palmprint Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 945-957, May 2011.
[22] Neurotechnology Inc., VeriFinger, http:/www.neurotechnology. com, 2012.
[23] 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.
[24] J. Feng, "Combining Minutiae Descriptors for Fingerprint Matching," Pattern Recognition, vol. 41, no. 1, pp. 342-352, 2008.
[25] Ron Smith and Associates, Inc. "Demystifying Palm Prints," http:/, 2012.
[26] "Directional Statistics—Wikipedia, the Free Encyclopedia," Wikipedia, , 2011.
[27] 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.
[28] "Hand—Wikipedia, the Free Encyclopedia," Wikipedia, 2011.
[29] G. Brunelli, "Stability of the First Carpometacarpal Joint," Finger Bone and Joint Injuries, pp. 167-174, Taylor & Francis, 1999.
[30] PolyU Palmprint Database, biometrics/, 2012.
[31] K. Bowyer, K. Hollingsworth, and P. Flynn, "Image Understanding for Iris Biometrics: A Survey," Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281-307, 2008.
[32] A. Martínez, "Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 748-763, June 2002.
[33] C.C. Han, H.L. Cheng, C.L. Lin, and K.C. Fan, "Personal Authentication Using Palm-Print Features," Pattern Recognition, vol. 36, no. 2, pp. 371-381, 2003.
[34] C. Han, "A hand-Based Personal Authentication Using a Coarse-to-Fine Strategy," Image and Vision Computing, vol. 22, no. 11, pp. 909-918, 2004.
[35] H. Dutağaci, B. Sankur, and E. Yörük, "Comparative Analysis of Global Hand Appearance-Based Person Recognition," J. Electronic Imaging, vol. 17, pp. 011018/1-011018/19, 2008.
[36] A.K. Jain and N. Duta, "Deformable Matching of Hand Shapes for Verification," Proc. Int'l Conf. Image Processing, 1999.
[37] D.H. Ballard, "Generalizing the Hough Transform to Detect Arbitrary Shapes," Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981.
[38] C. Watson, P. Grother, and D. Casasent, "Distortion-Tolerant Filter for Elastic-Distorted Fingerprint Matching," Proc. SPIE Optical Pattern Recognition, vol. 4043, pp. 166-174, 2000.
[39] R. Cappelli, D. Maio, and D. Maltoni, "Modelling Plastic Distortion in Fingerprint Images," Proc. Second Int'l Conf. Advances in Pattern Recognition, pp. 371-378, 2001.
[40] A. Senior and R. Bolle, "Improved Fingerprint Matching by Distortion Removal," IEICE Trans. Information and Systems, vol. 84, no. 7, pp. 825-832, 2001.
[41] A.K. Jain, L. Hong, and R.M. Bolle, "On-Line Fingerprint Verification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 302-314, Apr. 1997.
[42] A.K. Jain and J. Feng, "Latent Fingerprint Matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 88-100, Jan. 2011.
[43] A. Dempster et al., "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc. Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
[44] P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
[45] P. Viola, M. Jones, and D. Snow, "Detecting Pedestrians Using Patterns of Motion and Appearance," Int'l J. Computer Vision, vol. 63, no. 2, pp. 153-161, 2005.
[46] A. Ross, K. Nandakumar, and A. Jain, Handbook of Multibiometrics. Springer, 2006.
[47] "Biometric Testing and Statistics," NSTC Subcommittee on Biometrics, http://www.biometricscatalog.orgNSTCSub committee . 2006.
[48] X. Jiang and W.Y. Yau, "Fingerprint Minutiae Matching Based on the Local and Global Structures," Proc. Int'l Conf. Pattern Recognition, pp. 1038-1041, 2000.
[49] A.K. Jain, A. Ross, and S. Prabhakar, "Fingerprint Matching Using Minutiae and Texture Features," Proc. Int'l Conf. Image Processing, pp. 282-285, 2001.
[50] X. Chen, J. Tian, and X. Yang, "A New Algorithm for Distorted Fingerprints Matching Based on Normalized Fuzzy Similarity Measure," IEEE Trans. Image Processing, vol. 15, no. 3, pp. 767-776, Mar. 2006.
[51] A.K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, "Filterbank-Based Fingerprint Matching," IEEE Trans. Image Processing, vol. 9, no. 5, pp. 846-859, May 2002.

Index Terms:
statistical analysis,image matching,image registration,image segmentation,palmprint recognition,sensor fusion,palmprints gallery,ridge-based palmprint matching,biometric modality,palmprint recognition system,large-scale person identification,orientation field estimation,statistical analysis,segment-based matching,fusion algorithm,skin distortion,orientation field-based registration algorithm,coordinate system,cascade filter,palmprint regions,computational complexity reduction,palmprints registration,Algorithm design and analysis,Feature extraction,Accuracy,Humans,Skin,Bones,Computational complexity,naive Bayes classifier.,Palmprint,orientation field,density map,data fusion,distortion,matching,cascade filtering,generalized Hough transform
Jianjiang Feng, Jifeng Dai, Jie Zhou, "Robust and Efficient Ridge-Based Palmprint Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1618-1632, Aug. 2012, doi:10.1109/TPAMI.2011.237
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