Palmprint Identification Using FRIT


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Mobile Multimedia/Image Processing, Security and Applications, SPIE Defense, Security and Sensing,
Orlando, Florida, USA, 2011.

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Palmprint Identification Using FRIT

  1. 1. Palmprint Identification using FRIT Authors: Dakshina Ranjan Kisku, Ajita Rattani, Phalguni Gupta, *C. Jinshong Hwang, Jamuna Kanta Sing25 - 29 April 2011Orlando WorldCenter Marriott Presented By –Resort & Convention Prof. C. Jinshong HwangCenter Department of Computer Science, Texas State University,Orlando, Florida, San Marcos, Texas 78666, U.S.AUSA
  2. 2. Outline of Talk: Biometrics Palmprint Biometrics Hand Geometry Palm Characteristics Advantages of Palmprint Trait Proposed Palmprint Identification System ROI Extraction Feature Extraction using FRIT Classification with Bayesian Classifier Experimental Results CASIA Database IIT Kanpur Database Results Comparative Study
  3. 3. Biometrics: Biometrics authentication is a method by which one can be recognized based on one or more intrinsic physical or behavioral human characteristics.
  4. 4. Palmprint Biometrics:Hand Geometry Features: Hand shape and dimensions, finger size and lengthsPalm Characteristics [2], [16] Features: Principal lines, wrinkles and creases Principal lines: Heart line, head line and life line Wrinkles: Weaker and irregular lines, much thinner than principal lines Creases: More like fingerprint structure, have ridges and valleys
  5. 5. Advantages of Palmprint System:Advantages [2], [16]: High distinctiveness High permanence High performance Non – intrusiveness Low resolution imaging User – friendly Low price palmprint devices and low setup cost Highly stable
  6. 6. Proposed Palmprint IdentificationSystem: ROI [25] is detected and extracted from palm image. FRIT [24], [26] is applied on the ROI (region of interest) to extract a set of distinctive features from palmprint image. These features are used to classify with the help of Bayesian classifier [30]. The proposed system has been tested on CASIA [16], [28] and IIT Kanpur [31] palmprint databases.
  7. 7. Preprocessing: ROI ExtractionTo extract the ROI of palm image the following steps arefollowed:Convert the palm image to a binary image. Gaussian smoothing isused to enhance the image.Apply boundary-tracking algorithm in [25] to obtain the boundariesof the gaps between the fingers. Since the ring and the middlefingers are not useful for processing. Therefore, boundary of the gapbetween these two fingers is not extracted.
  8. 8. Contd…..ROI ExtractionDetermine palmprint coordinate system by computing the tangentof the two gaps with any two points on these gaps. The Y-axis isconsidered as the line which joining these two points. To determinethe origin of the coordinate system, midpoint of these two points aretaken through which a line is passing and the line is perpendicularto the Y-axis.Finally, extract ROI for feature extraction which is the central part ofthe palmprint.
  9. 9. Feature Extraction using FRIT: To characterize palmprint image Finite Ridgelet Transform [24], [26] is used to achieve a very compact representation of linear singularities. FRIT captures the singularities along lines and edges. As the continuous Ridgelet transform has close relations with other transforms in continuous domain, the continuous Ridgelet Transform for a given palmprint can be defined aswhere in 2-D are defined from a wavelet function in 1-Das
  10. 10. Contd……Therefore, the separable continuous wavelet transform of I(x, y) in ℜ 2space can be written as,
  11. 11. Contd…..The wavelets are functions with scale and line position and waveletsare found to be effective in representing point singularities.Ridgelets are found to be effective in representing singularities alongthe line.Ridgelets can be considered as concatenation of 1-D wavelets alongthe line.In particular, wavelets and Ridgelet transforms are related to Radontransform.Radon transform is a projection of image intensity along a radial lineoriented at a specific angle.It extracts lines in edge dominated images while palmprint imagesare considered to be an edge dominated images.
  12. 12. Contd….Therefore, for a given integrable bivariate function I(x, y), the Radontransform (RDT) is defined byContinuous Ridgelet transform (CRT) is considered as the application of1-D wavelet to the slices of Radon transform. Therefore,
  13. 13. Contd…..
  14. 14. Contd…..Each element in feature vector in Equation (11) can be obtained bywhere imax and jmax represent the total number of points in horizontaland vertical directions in the FRIT frame, respectively.
  15. 15. Classification using BayesianClassifier: In pattern recognition and classification field, Bayesian classifier [30] is found to be one of the best classifiers to classify the objects. Bayes error is the best criterion to evaluate feature sets. A posteriori probability functions are optimal features. Let x1 , x 2 , x3 ,..., x n denote the object classes and Ppalm is an palmprint image in feature space. Therefore, the posteriori probability function of xi given Ppalm is defined as
  16. 16. Contd…..where P( xi ) a priori probability, p( Ppalm | xi ) the conditional probability isdensity function of xi and p( Ppalm ) is the mixture density.The Maximum A Posteriori decision rule for the Bayes classifier is The palm image Ppalm is classified to xi of which the posteriori probability given Ppalm is the largest among all classes.The within class densities are usually modeled as normal distributions
  17. 17. Contd….where Mi and Σi are the mean and covariance matrix of class xi,respectively. From Equation (13) the decision function can be written asTherefore, a palmprint feature vector Ppalm is assigned to class xj if
  18. 18. Experimental Results:CASIA Database 5502 palmprint images / 312 subjects Left and right palms 8-bit gray scale JPEG images Taken with uniform-colored background Uniform distributed illumination Normalized to 140×140 pixelsIIT Kanpur Database 800 palmprint images / 400 subjects Resolution is set to 200 dpi Images are rotated by at most ±35 degree Images are normalized to 140×140 pixels
  19. 19. Contd….Table 1. Recognition Rates in CASIA Palmprint DatabaseTable 2. Recognition Rates in IIT Kanpur Palmprint Database
  20. 20. Contd…..Table 3. Comparative Study in terms of Error Rates
  21. 21. Conclusion: An efficient palmprint identification system has been presented in this paper where Finite Ridgelet Transform (FRIT) and Bayesian classifier are used. Shortcomings of wavelets are handled by the Finite Ridgelet Transforms and they extend the functionality of wavelets to higher singularities. Palmprints are classified using Bayesian classifier. All intra-class and inter-class comparisons are made for determining the recognition rates. The three rank based recognition rates are presented. The experimental results reveal better performance of the proposed system when it is compared with other existing systems.
  22. 22. References:1. Zhang, L., and Zhang, D., “Characterization of palmprints by wavelet signatures via directional context modeling,” IEEE Transactions on Systems, Man and Cybernetics – B, 34(3), 1335 – 1347 (2004).2. Han, C. C., Cheng, H. L., Lin, C. L., and Fan, K. C., “Personal authentication using palmprint features,” Pattern Recognition 36(2), 371 – 381 (2003).3. Lin, C. L., Chuang, T. C., and Fan, K. C., “Palmprint verification using hierarchical decomposition,” Pattern Recognition 38(12), 2639 – 2652 (2005).4. Wu, X. Q., Zhang, D., Wang, K. Q., and Huang, B., “Palmprint classification using principal lines,” Pattern Recognition 37(10), 1987 – 1998 (2004).5. Wu, X. Q., Zhang, D., and Wang, K. Q., “Palm line extraction and matching for personal authentication,” IEEE Transactions on Systems, Man and Cybernetics – A, 36(5), 978 – 987 (2006).6. Liu, L., and Zhang, D., “A novel palm-line detector,” Proceedings of the 5th AVBPA, 563 – 571 (2005).7. Liu, L., Zhang, D., and You, J., “Detecting wide lines using isotropic nonlinear filtering,” IEEE Transactions on Image Processing, 16(6), 1584 – 1595 (2007).8. Connie, T., Jin, A. T. B., On, M. g. K., and Ling, D. N. C., “An automated palmprint recognition system,” Image Vision Computing, 23(5), 501 – 515 (2005).9. Ribaric, S., and Fratric, I., “A biometric identification system based on eigenpalm and eigenfinger features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 1698 – 1709 (2005).10. Yang, J., Zhang, D., Yang, J. Y., and Niu, B., “Globally maximizing locally minimizing: Unsupervised Discriminant Projection with applications to face and palm Biometrics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 650 – 664 (2007).
  23. 23. Contd…..11. Shang, L., Huang, D. S., Du, J. X., and Zheng, C. H., “Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network,” Neurocomputing, 69(13), 1782 – 1786 (2006).12. Han, D., Guo, Z., and Zhang, D., “Multispectral palmprint recognition using wavelet-based image fusion,” International Conference on Signal Processing, 2074 – 2077 (2008).13. Zhang, D., Guo, Z., Lu, G., Zhang, L., and Zuo, W., "An online system of multi-spectral palmprint verification", IEEE Transactions on Instrumentation and Measurement, 59(2), 480 – 490 (2010).14. Kong, A., and Zhang, D., “Competitive coding scheme for palmprint verification”, International Conference on Pattern Recognition, 1, 520 – 523 (2004).15. Sun, Y. H. Z., Tan, T., and Ren, C., “Multi-spectral palm image fusion for accurate contact- free palmprint recognition,” IEEE International Conference on Image Processing, 281 – 284 (2008).16. Zhang, D., Kong, W. K., You, J., and Wong, M., “On-line palmprint identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1041 – 1050 (2003).17. Kumar, A., Shen, and H. C., “Palmprint identification using palmcodes,” International Conference on Image & Graphics, 258 – 261 (2004).18. You, J., Li, W., and Zhang, D., “Hierarchical palmprint identification via multiple feature extraction,” Pattern Recognition, 35, 847 – 859 (2002).19. Li, W., Zhang, D., and Xu, Z., “Palmprint identification by Fourier transform,” International Journal of Pattern Recognition & Artificial Intelligence, 16(4), 417 – 432 (2002).20. Kumar, A., and Shen, H. C., “Recognition of palmprints using wavelet-based features,” International Conference on Systems and Cybernetics (2002).
  24. 24. Contd…..21. Zhang, L., and Zhang, D., “Characterization of palmprints by wavelet signatures via directional context modeling,” IEEE Transactions on Systems, Man and Cybernetics – B, 1335 – 1347 (2004).22. Bosnjak, A., Montilla, G., and Torrealba, V., “Medical images segmentation using Gabor filters applied to Echocardiographic images,” Computers in Cardiology, 25, 457 – 460 (1998).23. Pun, C., and Lee, M., “Log-Polar wavelet energy signatures for rotation and scale variant texture classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5) (2003).24. Do, M., and Vetterli, M., “The finite ridgelet transform for image representation,” IEEE Transactions on Image Processing, 12, 16 – 28 (2003).25. Zhang, D., Kong, W. K., You, J., and Wong, M., “On-line palmprint identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1041 – 1050 (2003).26. Candµes, E. J., [Ridgelets: Theory and Applications], Ph.D. thesis, Department of Statistics, Stanford University, 1998.27. Candµes, E. J., and Donoho, and D. L., “Ridgelets: a key to higher-dimensional intermittency?," Philosophical Transactions of Royal Society Lond. – A, 2495 – 2509 (1999).28. Kong, W., and Zhang, D., “Feature-level fusion for effective palmprint authentication,” In: Zhang, D., Jain, A.K. (eds.) ICBA 2004 LNCS, 3072, 761 – 767 (2004).29. Kong, W., and Zhang, D., “Competitive coding scheme for palmprint verification,” International Conference Pattern Recognition, 1, 520 – 523 (2004).30. Moghaddam, B., Jebara, T., and Pentland, A., “Bayesian face recognition,” Pattern Recognition, 33(11), 1771 – 1782 (2000).31. Kisku, D. R., Gupta, G., Sing, J. K., "Feature level fusion of face and palmprint biometrics by isomorphic graph-based improved K-medoids partitioning," 4th International Conference on Information Security and Assurance Lecture Notes in Computer Science, 6059, 70 – 81 (2010).
  25. 25. Questions ???
  26. 26. Thank you !Contact