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Palmprint verification using lagrangian decomposition and invariant interest


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DSS 2011

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Palmprint verification using lagrangian decomposition and invariant interest

  1. 1. Palmprint Verification using Lagrangian Decomposition and Invariant Interest Points Authors: P. Gupta, A. Rattani, D. R. Kisku, *C. J. Hwang, J. K. Sing Presented by - C. J. Hwang Department of Computer Science, Texas State University, San Marcos, Texas 78666, U.S.A 25 - 29 April 2011 Orlando World Center Marriott Resort & Convention Center Orlando, Florida, USA
  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 SIFT • Palmprint Matching using Graph • Experimental Results – CASIA Database – IIT Kanpur Database – Results – Conclusion
  3. 3. Biometrics System: • 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 lengths • Palm Characteristics – 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: – 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 Identification System: • ROI [25] is detected and extracted from palm image. • Uniform intensity distribution is obtained by applying histogram equalization • SIFT is applied to the ROI (region of interest) of palmprint image to extract invariant features • Palmprint matching is performed using Lagrangian decomposition and graph matching technique
  7. 7. ROI Extraction: To extract ROI of palm image the following steps are followed: • Convert the palm image to a binary image. Gaussian smoothing is used to enhance the image. • Apply boundary-tracking algorithm to obtain the boundaries of the gaps between the fingers. Since the ring and the middle fingers are not useful for processing. Therefore, boundary of the gap between these two fingers is not extracted.
  8. 8. Contd…..ROI Extraction • Determine palmprint coordinate system by computing the tangent of the two gaps with any two points on these gaps. The Y-axis is considered as the line which joining these two points. To determine the origin of the coordinate system, midpoint of these two points are taken through which a line is passing and the line is perpendicular to the Y-axis. • Finally, extract ROI for feature extraction which is the central part of the palmprint.
  9. 9. Feature Extraction using SIFT: The scale invariant feature transform, called SIFT descriptor, has been proposed by and proved to be invariant to image rotation, scaling, partly illumination changes and projective transform. The basic idea of the SIFT descriptor is detecting feature points efficiently through a staged filtering approach that identifies stable points in the scale-space.
  10. 10. Contd…. Local feature points are extracted from the following steps: • Scale-space extrema detection: select candidates for feature points by searching peaks in the scale-space from a difference of Gaussian (DoG) function • Keypoint localization: localize the feature points by using the measurement of their stability • Orientation assignment: assign orientations based on local image properties • Keypoint descriptor: calculate the feature descriptors which represent local shape distortions and illumination changes.
  11. 11. Contd…. • Palmprint Image • Palmprint Image After Applying Histogram Equalization • SIFT Features Extraction from Enhanced Palmprint Image. 50 100 150 200 250 300 50 100 150 200 250
  12. 12. Palmprint Matching using Lagrangian Network Graph: Problem formulation: • Let G1 and G2 be two graphs obtained from a pair of palmprint images after having extracted SIFT features • A permutation matrix is determined from the pair of graphs and this permutation matrix is used to minimize the distance between these graphs. • Permutation matrix is a zero-one matrix whose rows and columns sum to one. Rows and columns can add up to one or zero. • In the deterministic annealing framework, permutation matrix constraints can be formulated. The rows and columns constraints are known as winner- take-alls (WTAs). • The proposed approach gets inspired by Lagrangian decomposition approach in which the rows and columns constraints are satisfied separately by Lagrange multipliers which are used to equate the two solutions.
  13. 13. Contd…. • Let us consider, G1x1y1 and G2x2y2 are the two adjacency matrices of two graphs G1(V,E) and G2(V,E), respectively. Now, a permutation matrix M will be determined which will minimize the distance between the two graphs. • The adjacency matrices of the two undirected graphs can be represented as symmetric and sparse matrices with zero diagonal entries. • The problem can be defined as follows ∑ ∑ ∑         − 21 2 1 2 22211111 21min xx y y xyyxxyyx M GMMG ∑ ∑ == 1 2 2121 1,1 x x xxxx MM
  14. 14. Contd…. • The match matrix M contained in the distance measure must satisfy the permutation matrix constraints. In addition to these constraints it also includes row and column WTAs ∑ ∑ == 1 2 2121 )1,1( x x xxxx MandM • Let us consider two match matrices are given as and which have the following properties, )1( 21xxM )2( 21xxM ∑ ∑ == 1 2 2121 1,1 x x xxxx MandM • In the given constrains these properties are always satisfied. The properties given in above equation can be established by taking a new objective which is given by ∑ ∑∑         −= 21 2 2 22 )2( 21 1 )1( 2111 )2()1( , 21),(min )2()1( xx y xyyx y xyyx MM GMMGMMD
  15. 15. Contd…. subject to • The constraint given in the above equation can be established using a Lagrange parameter λ. • Finally, the distance is compared with a predefined threshold and accordingly decision of acceptance or rejection is made. )2( 21 )1( 21 xxxx MM =
  16. 16. Evaluation: Databases • 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 150×150 pixels • IIT 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 150×150 pixels
  17. 17. Contd….Experimental Results Table 1. FRR, FAR and Recognition Rates Determined on CASIA and IIT Kanpur Databases 97.140.984.73IIT Kanpur Database 95.82.396.01CASIA Database RECOGNITION RATE (%) FAR (%)FRR (%)DATABASE
  18. 18. Conclusion: • In this paper, a palmprint based verification system using SIFT features and Lagrangian network graph technique has been proposed. • Region of interest (ROI) has been extracted from the wide palm texture at the preprocessing stage and histogram equalization technique is applied to palmprint image for obtaining uniform intensity. • At the next stage, SIFT feature extraction is performed on palmprint image, whereas only the ROI is considered for invariant points extraction. • Finally, identity is established by finding permutation matrix for a pair of reference and probe palm graphs drawn on extracted SIFT features. Permutation matrix is used to minimize the distance between two graphs. • The experimental results computed on CASIA and IITK palmprint databases show the effectiveness and the robustness of the proposed system.
  19. 19. 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] 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). [3] 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). [4] Lowe, D., “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, 60(2), 91 – 110 (2004). [5] Christmas, W, J., Kittler, J., and Petrou, M., “Structural matching in computer vision using probabilistic relaxation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 17, 749 – 764 (1995). [6] 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). [7] Kisku, D. R., Gupta, P., and 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). [8] Han, Y.F., Sun, Z.N., and Tan, T.N., “Palmprint recognition based on directional features and graph matching,” International Conference on Biometrics, LNCS, 4642, 1164 – 1173 (2007). [9] Jain, A. K., and Jianjiang, F., “Latent palmprint matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 1032 – 1047 (2009) [10] Kostin, A., Kittler, J., and Christmas, W. J., “Object recognition by symmetrised graph matching using relaxation labelling with an inhibitory mechanism,” Pattern Recognition Letters, 26(3), 381 - 393 (2005) [11] Gold, S., and Rangarajan, A., “A graduated assignment algorithm for graph matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(4), 377–388 (1996). [12] Ullman, J. R., “An algorithm for subgraph isomorphism,” Journal of ACM, 23(1), 31 – 42 (1976). [13] Jornsten, K., and Nasberg, M., “A new Lagrangian relaxation approach to the generalized assignment problem,” European Journal of Operational Research, 27, 313 – 323 (1986). [14] Guignard, M., and Kim, S., “Lagrangean decomposition: A model yielding stronger Lagrangian bounds,” Mathematical Programming, 39, 215 – 228 (1987).
  20. 20. Questions ???
  21. 21. Thank You !!! Contact Author: drkisku@ieee.orgContact Author: drkisku@ieee.orgContact Author: drkisku@ieee.orgContact Author: