Rattani - Ph.D. Defense Slides


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Rattani - Ph.D. Defense Slides

  1. 1. Adaptive Biometric Systems based on Template Update Paradigm Ajita Rattani University of Cagliari, Department of Electrical and Electronic Engineering, ajita.rattani@ diee.unica.it Supervisors: Prof. Fabio Roli and Dr. Gian Luca Marcialis P R A G
  2. 2. What is Biometrics? Automatic recognition of person based on their distinctive anatomical and behavioral characteristics like face and fingerprint. Fingerprint Face Signature Voice Hand geometry Facial Retinal scan Iris Gait thermogram 2
  3. 3. Biometric Phases Enrollment Phase Verification/ Identification Phase 3
  4. 4. Enrollment Phase Enrollment Phase x, y, theta x, y, theta Feature x, y, theta “ x, y, theta Extraction x, y, theta x, y, theta Storage Extracted Mr. X Features Database Template 4
  5. 5. Verification Phase Database Template yes Feature Matching Score or Score > extraction m odule distance threshold Input Query no Accepted Rejected 5
  6. 6. Problem: Intra-Class Variations 6
  7. 7. Template Representativeness Enrolled templates: usually captured in controlled environment Input Query : Substancial intra-class variation Effect: Making enrolled templates ‘Un-representative’ 7
  8. 8. Standard Solutions Multi-biometric Storing multiple templates (multi-instance) Using Multi-modalities Repeating the process of enrollment over time 8
  9. 9. Multibiometric Super Template Multi-Modality A. Rattani, D. R. Kisku, A. Lagorio and M. Tistarelli, “Facial Template A. Rattani, D. R. Kisku, M. Bicego and M. Tistarelli, “Feature Level Fusion Synthesis Based on SIFT Features”, Automatic Identiffication Advanced of Face and Fingerprint”, Biometrics: Theory, Applications and Systems (BTAS 2007), 1-6, Technologies (AUTOID) 2007 IEEE Workshop, 69-73, Alghero, Italy, 2007 Washington, USA 9
  10. 10. Template Update: Solution to Representativeness Standard Solutions: Fails to capture Temporal Intra-class variations Novel Solutions : “Template Update” procedure/ Adaptive biometric systems Aim: Update enrolled templates to the intra-class variation of the input data 10
  11. 11. State of Art: template update Not Mature Enough No mention of the learning methodology involved No investigation of the pros, cons and open issues Lack of clear statement of the problem 11
  12. 12. Goal of PhD Studies Formulate the taxonomy of the current state of art template update methods Pros and Cons of State of Art Update Methods Effect of update procedures on different group of users (‘Doddington Zoo’) Proposal of Novel solution 12
  13. 13. Ajita Rattani, Biagio Freni, Gian Luca Marcialis, Fabio Roli , “Template Update Methods in Adaptive Biometric Systems: A Critical Review", 3rd IEEE/IAPR International Conference on Biometrics ICB 2009, Alghero (Italy), Springer, 02/06/2009 Template based Adaptive Biometric System Semi-supervised Supervised Multiple Single Modality Template Selection Modality Co-training Editing Self-training Clustering based Graph based Mincut Online Offline Feature Selection 13
  14. 14. State of the Art (Template Update) Supervised Learning (Uludag et al., PR 2004) Offline process Limitations: Tedious, time consuming Inefficient for repeated updating task 14
  15. 15. ….Contd Semi-Supervised Learning Initial labelled + Unlabelled input data (“Automatic Self Update”) Online Updating Jiang and Ser, PAMI 2002; Ryu et al., ICPR 2006 Offline Updating Roli and Marcialis, SSPR 2006, Roli et al., ICB 2007 15
  16. 16. Template Co-update: A Conceptual Example Initial template Unlabeled Samples Roli et al. (ICB2007) Difficult face sample ple 16
  17. 17. Protocol followed for Experimental Investigation For Database of size N x M One sample : Initial template Remaining M-1 samples are divided into Unlabelled and Test set Equal number of impostor samples are added: Unlabelled and Test Set Unlabelled set (Du): for updating the templates Test set: measures the performance enhancement after updating 17
  18. 18. An Experimental Analysis on Pros and Cons of Self-update and Co-update Performance comparison of the Co-update with Self update Representativeness of the enrolled templates Controlled and Un-controlled environment Can operation at relaxed threshold help “self-update” to capture difficult patterns? • Ajita Rattani, Gian Luca Marcialis and Fabio Roli, Capturing large intra-class variation of the biometric data by template co- updating,IEEE Workshop on Biometrics, Int. Conference on Vision and Pattern Recognition CVPR 2008, Anchorage (Alaska, USA), IEEE, pp. 1-6, 23/07/2008 • A. Rattani, G.L. Marcialis, F. Roli, Boosting gallery representativeness by co-updating face and fingerprint verification systems, Best Paper Award at 5th International School for Advanced Studies on Biometrics for Secure Authentication, June, 9-13, 2008, Alghero (Italy). 18
  19. 19. Co-updating vs. Self-update: Un-controlled Environment; EER point of view 30 Face Self-Update 14 Finger Self-Update face self-update Face Co-update face co-update finger self-update 25 Finger Co-update 12 finger co-update 10 20 EER (%) EER (%) 8 15 6 10 4 2 5 0 50 100 150 200 250 300 0 50 100 150 200 250 300 350 # No. of unlabelled data added # No. of unlabelled data added Shows EER on the test set as a function of the amount of unlabelled data exploited by template self and co-update algorithms at each iteration. The curve of the self update is shorter due to non-exploitation of much unlabelled data because of operation at high threshold. 19
  20. 20. Galleries Images as captured by Self- update and Co-update Differences with Self-update: More Unlabelled samples added Larger intra-class variations introduced even at initial stages Initial 19 template initial accuracy face self-update at varying threshold 18 17 16 EER (%) 15 Initial 14 template 13 12 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 %FAR used for selecting threshold for unlabelled data 20
  21. 21. Local Update Behaviour of Self-update 21
  22. 22. Remarks Template Co-update: Non-Representative templates: Can capture large intra-class variations Representative templates: Comparable performance of Self-update and Co- update Self-updating : very much dependent on the initial templates. Un-representative initial templates: Results in poor capture of difficult samples due to operation at stringent threshold However, operation at relaxed threshold results in counter -productive effect Ajita Rattani, Gian Luca Marcialis and Fabio Roli, Capturing large intra-class variation of the biometric data by template co- updating,IEEE Workshop on Biometrics, Int. Conference on Vision and Pattern Recognition CVPR 2008, Anchorage (Alaska, USA), IEEE, pp. 1-6, 23/07/2008 22
  23. 23. Open Issues Unexplored Effect of Creep in errors (‘impostor introduction’) Effect of different types of updating threshold Analysis of the effect of user population on template update procedure 23
  24. 24. Difficult Clients and “Doddington’s zoo” Doddington et al. (1998) introduced some terms to indicate clients wrongly classifiable even at high thresholds Lambs: “easy-to-imitate” clients High FAR when attacked Wolves: they can easily imitate other clients A wolf into a client’s gallery may attract other wolves Goats: difficult to be recognized A goat may not be able to update itself Sheeps: Well behaved Clients 24
  25. 25. User Population Characteristics Hypothesis: Apart from basic FAR of the system, impostors may be introduced due to the presence of wolves and lambs Effect of template updating may not be same because of the presence of “Doddington zoo” 25
  26. 26. Goal of the work Experimental evaluation of the impact of impostors introduction in on- line self update At different settings of updating threshold Fixed/Dynamic Global/User-specific Stringent/Relaxed Presence of intrinsically “difficult” clients Non-uniform effect of update procedures on different charateristic clients 26
  27. 27. EER vs. impostors introduction at 1% updating threshold 34 25 Fixed Non-user specific Fixed Non-user specific Updated Non-user specific Updated Non-user specific Fixed User specific Fixed User specific 32 Updated User-Specific 20 Updated User-Specific Equal Error Rate (EER) 30 % of impostors 15 28 10 26 24 5 22 0 100 200 300 400 500 600 0 # of Unlabelled data used 0 100 200 300 400 500 600 # of Unlabelled data used Gian Luca Marcialis, Ajita Rattani and Fabio Roli, Biometric template update: An experimental investigation on the relationship between update errors and performance degradation in face verification, Joint IAPR Int. Workshop on Structural and Syntactical Pattern Recognition and Statistical Techniques in Pattern Recognition S+SSPR08, Orlando (Florida, USA), Springer, 04/12/2008 27
  28. 28. Performance Evaluation of Self-Update After Division of Database on the basis of Doddington Zoo 1. Lambs 2. Sheeps 100 100 Ajita Rattani, Gian Luca Marcialis After Updating After Updating and Fabio Roli, "An Experimental Before Updating Before Updating Analysis of the Relationship between Biometric Template Update and the (%) FRR (%) FRR 50 50 Doddington’s Zoo in Face Verification", ICIAP 2009, Salerno (Italy) 0 0 0 50 100 0 50 100 (%) FAR (%) FAR 3. Goats 4. Wolves 100 100 After Updating After Updating Before Updating Before Updating (%) FRR (%) FRR 50 50 0 0 0 50 100 0 50 100 (%) FAR (%) FAR 28
  29. 29. “Attraction” path Unlabelled samples iteratively added to the gallery Initial template First impostor Other wolves (wolf) are added 29
  30. 30. Remarks For first-time the effect of misclassification errors in self update process It resulted to be very much dependent on the threshold type settings and the security level for acceptance of input data Impostors inclusion cannot be avoided even at strict threshold settings (zeroFAR) The presence of different animals result in different updating effects 30
  31. 31. Open Issues Still Remained! As Analyzed : Current state of art methods are capable of capturing only near input images Operation at relaxed threshold results in increased probability of impostors introduction Need: Investigation of more robust update procedures with the following characteristics Capture of large intra-class variations without increasing probability of impostors Not increasing the probability of impostors introduction 31
  32. 32. Graph based Semi-Supervised Learning Self-update methods : ‘Local’ update behaviour Graph based methods to Semi-supervised methods : Application: Machine Learning literature like Image Segmentation , Pattern Recognition These methods can study the global structure of the data manifold Hypothesis: Graph based learning may capture large intra-class variations Mincut based labelling is a binary technique assigning labels by finding min-cut
  33. 33. “Well-connected” and “Separated” hypothesis Region as a set of different people (expressions, lighting, poses) Graph-mincut can better assign labels to each region, even with a small amount of labelled samples (Blum and Chawla, 2001) by studing underlying structure in the form of graph. A. Rattani, G.L. Marcialis, F. Roli, Biometric template update using the graph-mincut algorithm: a case study in face verification, IEEE Biometric Symposium BioSymp08, September, 23-25, 2008, Tampa (Florida, USA), IEEE, ISBN 978-1-4244-2567-9, pp. 23- 28. 33
  34. 34. Basic Graph based Mincut Graph G= (V, E) ; V= {L, U, v+, v-} {v+, v-}: Two classification vertices, null nodes representing “positive” and “negative” classes. E : edge defining function, basis on which two nodes are connected Aim : partition v+ from v- by finding the cut on the minimum similarity set of edges. 34
  35. 35. Graph Theory: Working of Mincut V E V+ V- 1. Given an initial Graph 35
  36. 36. ….Contd 2. All possible s-t paths are traversed 36
  37. 37. ….Contd 3. Flow is increased by an amount which different capacity edges can take 37
  38. 38. ….Contd Last step: All the nodes reacheable from source are classified as positive 38
  39. 39. Why Graph Mincut may Work ? Global structure of manifold is analyzed: By traversing all s-t paths Minimum capacity edges are saturated first Probability of impostor introduction is minimized 39
  40. 40. An Hypothetical Example: 40
  41. 41. Samples Exploited for Updating : Self Update and Mincut % Impostors Encountered % Samples Encountered A. Rattani, G.L. Marcialis, F. Roli, Biometric template update using the graph-mincut algorithm: a case study in face verification, IEEE Biometric Symposium BioSymp08, September, 23-25, 2008, Tampa (Florida, USA), IEEE, ISBN 978-1-4244-2567-9, pp. 23-28. 41
  42. 42. Concluding Remarks Critical survey on the template update procedure Pros and cons of state of art methods Studied the effect of impostor introduction Proposed novel solutions 42
  43. 43. Future Work Modeling of probability of impostor introduction The use of quality information of an input sample: Quality measures are an array of measurements of conformance of biometric samples to some predefined criteria known Genuine Intra-class variation? 43
  44. 44. …Contd Modeling of Appropriate Stopping criteria for Template Updating Use of Cohort information in template updating Norman et al. 2009 44
  45. 45. …Contd Robust criteria for selection of input data for updating: F- Ratio or d-prime FRatio=(µ Gen-µ Imp) ⁄ (σGen+ σImp) D-prime=(µ Gen-µ Imp)/(σ) Evaluation on “Large Scale Databases” 45
  46. 46. 46