IEEE ICAPR 2009

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Multisensor Biometric Evidence Fusion for Person Authentication using Wavelet Decomposition and Monotonic-Decreasing Graph

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IEEE ICAPR 2009

  1. 1. “ Multisensor Biometric Evidence Fusion for Person Authentication using Wavelet Decomposition and Monotonic-Decreasing Graph” *D. R. Kisku, J. K. Sing, M. Tistarelli, P. Gupta *Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur – 713206, India [email_address]
  2. 2. Agenda of discussion: <ul><li>Introduction </li></ul><ul><li>Multisensor biometric evidence fusion using wavelet decomposition </li></ul><ul><li>SIFT features extraction </li></ul><ul><li>Mono-tonic decreasing graph </li></ul><ul><li>Experimental results </li></ul><ul><li>Concluding remarks </li></ul>
  3. 3. Introduction: <ul><li>This work presents a novel biometric sensor generated evidence fusion of face and palmprint images using wavelet decomposition for personnel identity verification. </li></ul><ul><li>The approach of biometric image fusion at sensor level refers to a process that fuses multi-pattern images captured at different resolutions and by different biometric sensors to acquire richer and complementary information to produce a new fused image in spatially enhanced form. </li></ul><ul><li>When the fused image is ready for further processing, SIFT operator are then used for feature extraction and the recognition is performed by adjustable structural graph matching between a pair of fused images by searching corresponding points using recursive descent tree traversal approach. </li></ul>
  4. 4. Multisensor biometric evidence fusion using wavelet decomposition: <ul><li>Multisensor image fusion [1] refers to a process that fuses images to generate a complete fused image at low level. </li></ul><ul><li>Fused image contains redundant and complementary richer information. </li></ul><ul><li>Evidence fusion is based on the image decomposition [1] into multiple-channel depending on their local frequency. </li></ul><ul><li>Decompose image into a number of new images, each of them having a different degree of resolution. </li></ul><ul><li>According to Fourier transform, the wave representation is an intermediate representation between Fourier and spatial representations. </li></ul><ul><li>It has the capability to provide good localization for both frequency and space domains. </li></ul>
  5. 5. Contd… <ul><li>Wavelet based image fusion [1] of face and palmprint images is shown in the following Figure. </li></ul>Face image Palm image Decomposition Decomposition Fusion of decompositions Fused image
  6. 6. SIFT feature extraction: <ul><li>The scale invariant feature transform, called SIFT descriptor, has proposed by David Lowe [2] and proved to be invariant to image rotation, scaling, translation, partly illumination changes. </li></ul><ul><li>The investigation of SIFT features for biometrics has been explored in [3]-[4]. </li></ul><ul><li>SIFT feature points are detected with the following steps: </li></ul><ul><li>select candidates for feature points by searching peaks in the scale-space from a difference of Gaussian (DoG) function, </li></ul><ul><li>localize the feature points by using the measurement of their stability, </li></ul>
  7. 7. Contd… <ul><li>assign orientations based on local image properties, </li></ul><ul><li>calculate the feature descriptors which represent local shape distortions and illumination changes. </li></ul>Left image shows the fused image and on the right, SIFT feature extraction is shown from the fused image.
  8. 8. Structural graph for matching and verification: <ul><li>Monotonic-decreasing graph based [5] relation is established between a pair of fused images. </li></ul><ul><li>A recursive tree traversal algorithm is used for searching the paired matching feature points. </li></ul><ul><li>We choose a set of three points on a given fused gallery image, which are uniquely determined. </li></ul><ul><li>Connecting these three points with each other and form a triangle, and also three distances are computed. </li></ul><ul><li>We try to locate another set of three points on a given fused probe image that also form a triangle, which is the best matching the triangle computed on gallery image. </li></ul>
  9. 9. Contd… <ul><li>Best match would be possible when the edges of second triangle will be matched of the edges of the first triangle maintaining the following criterion </li></ul><ul><li>Traversal would be possible when one of the first vertices and the subsequent vertices of the second triangle may correspond to the first vertex and the subsequent vertices of the first triangle and conversely, may also possible . </li></ul>
  10. 10. Contd… <ul><li>Traversal can be start from the first edge (pi, pj) and by visiting n feature points, we can generate a matching graph on the fused probe image which should be a corresponding candidate graph of G . </li></ul><ul><li>At the end of traversal algorithm, a set of candidate graphs are found with each of identical number of feature points. </li></ul><ul><li>For illustration, consider with the minimal k-th order error from, the final optimal graph can be found from the set of candidate graphs and we can write, </li></ul>
  11. 11. Contd… <ul><li>The k-th order error between the optimal graph and the gallery graph can be computed as </li></ul><ul><li>The above equation denotes the sum of all differences between a pair edges corresponding to a pair of graphs. </li></ul><ul><li>For identity verification of a person, client-specific threshold has been determined heuristically for each user, and the final dissimilarity value is then compared with client-specific threshold and decision is made </li></ul>
  12. 12. Experimental results: <ul><li>The experiment of the proposed method is carried out on multimodal database containing the face and palmprint images of 150 individuals. </li></ul><ul><li>The matching is accomplished for the proposed method and the results shows that fusion performance at the semi-sensor level / low level is found to be superior when it is compared with other two methods, namely, palmprint verification and face recognition drawn on same feature space. </li></ul><ul><li>Multisensor biometric image fusion produces 98.19% accuracy, while face recognition and palmprint recognition systems produce 89.04% accuracy and 92.17% accuracy, respectively, as shown in the Figure. </li></ul>
  13. 13. Contd… ROC curves (in ‘stairs’ form) for the different methods are shown.
  14. 14. Concluding remarks: <ul><li>A novel and efficient method of multisensor biometric image fusion of face and palmprint for personal authentication is proposed. </li></ul><ul><li>High-resolution face and palmprint images are fused using wavelet decomposition process and matching is performed by monotonic-decreasing graph drawn on invariant SIFT features. </li></ul><ul><li>The result shows that the proposed method initiated at the low level / semi-sensor level is robust, computationally efficient and less sensitive to unwanted noise confirming the validity and efficacy of the system </li></ul>
  15. 15. References: <ul><li>T. Stathaki, “Image Fusion – Algorithms and Applications”, Academic Press, U.K., 2008. </li></ul><ul><li>D. G. Lowe, “Distinctive image features from scale invariant keypoints”, International Journal of Computer Vision, vol. 60, no. 2, 2004. </li></ul><ul><li>U. Park, S. Pankanti and A. K. Jain, &quot; Fingerprint Verification Using SIFT Features &quot;, Proceedings of SPIE Defense and Security Symposium, Orlando, Florida, 2008. </li></ul><ul><li>M. Bicego, A. Lagorio, E. Grosso and M. Tistarelli, “On the use of SIFT features for face authentication”, Proc. of Int Workshop on Biometrics, in association with CVPR 2006. </li></ul><ul><li>Z. C. Lin, H. Lee and T. S. Huang, &quot;Finding 3-D point correspondences in motion estimation&quot;, Proceeding of International Conference on Pattern Recognition, Paris, France, pp.303 – 305, October, 1986. </li></ul>
  16. 16. <ul><li>Questions ? ? ? </li></ul>

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