Self adaptive biometric systems


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Self adaptive biometric systems

  1. 1. Self Adaptive Systems: An ExperimentalAnalysis of the Performance Over Time Ajita Rattani, Gian Luca Marcialis, Fabio Roli
  2. 2. Biometric Verification Systems They operate in two distinct stages: Image is acquired for each user (gallery) in controlled environment 1) The enrolment stage (for instance ISO/ IEC FCD 19794-5 standard) 2) The verification stage. “Template” is created and identity labels assignedThe performance of biometric systems degrades quickly when input imagesexhibit substantial variations compared to the enrolled “templates”  Some face examples showing intra-class variations in input data un- represented by enrolled template TEMPLATE QUERY IMAGES 2
  3. 3. Initial Attempts to Increase Template Representativeness• Re-enrollment• Multibiometrics (Handbook of Multi-biometrics, 2006)• Virtual biometric template synthesis (pose correction, illumination correction, de-ageing transformations) (Wang et al. 2006, Geng et al. 2007) 3
  4. 4. Recent Introduction- Template update Methods• Characteristics: – Adapt themselves to the intra-class variation of the input data. – Minimizing performance loss due to unrepresentative and outdated templates• Commonly adopted is self-adaptive systems. 4
  5. 5. Self-Adaptive Systems • Highly confidently classified samples are used for adaptation • In order to avoid impostor introduction • Claimed to be robust against short and medium term intra- class variations
  6. 6. State-of-the-artReference Modality Impostor DatabaseX. Jiang Finger No 100x8and W. Ser 12x200Roli et al Face No 100x8Ryu et al. Finger No 41x100Pavani et Face No 5 monthsal. Till date: No paper has shown the performance robustness over time Reason: Unavailability of large number of samples collected over a period of time, per user basis Assumption of absence of impostor No theoretical explanation of the functioning
  7. 7. Contributions– This is the first study evaluating the performance of self-adaptive systems, on the input batch of samples as available over time– The conceptual explanation of the functioning of self-adaptive systems, supported by experimental validations– DIEE multimodal database has been explicitly collected for this aim, over a span of 1.5 years
  8. 8. Conceptual representationA hypothetical diagram showing the The representational capability of eachinitial condition where the enrolled template in the updated set ontemplate is shown with the help of star adaptation using samples 1, 2 and 3and encircled in its representation region
  9. 9. Contd...As a result overall genuine region In the real time environment impostorexpands samples may also be present
  10. 10. Experimental Validations• Dataset: DIEE Multimodal database – 49 subjects with 50 samples per subject acquired in five sessions with 10 samples per session – Acquired in a time span of 1.5 years – Containing temporal as well as other intra-class variations Example facial images taken from two different sessions for a randomnly choosen user
  11. 11. Experimental Protocol• Training: 2 enrolled images per person from the batch b_1• Updating: batches two to four• Performance evaluation:On updating using batch b_i performance is evaluated for batch b_i+1and EER_i computed
  12. 12. Experiment #1• Aim: to evaluate the performance of self adaptive systems over time• Assumption of absence of impostor’s access.• Updating threshold: 0.01 % FAR
  13. 13. Results Performance enhancement and stability can be attained over timePerformance of self-adaptive systems for index andthumbprint biometrics in comparison to baseline classifier
  14. 14. At varying threshold conditions Large variation in the performance from one updating cycle to another as a result of representation region expansion significantly Performance of self-adaptive face recognition system at varying thresholds from stringent to relaxed for face biometrics
  15. 15. Table: showing percentage of samples graduallyadded to the user’s gallery for face biometrics Threshold (%) Cycle 1 (%) Cycle 2 (%) Cycle 3 (%) Cycle 4 at % FAR 0.00001 % 31.17 21.60 19.75 19.36 0.00001 % 33.3 26.54 26.95 27.70 0.01 % 52.16 55.86 61.83 65.74 0.1 % 56.79 62.03 68.31 70.98 1% 60.8 68.51 73.66 75.30
  16. 16. Further confirmation:- representation region expansionThe scores obtained on fifth batch using the The scores obtained on fifth batch using thebaseline matcher enrolled with two templates self-adaptive system updated using 1 to 4from a random user for thumb biometric batches for a random user for thumb biometric
  17. 17. Experiment #2• Aim: is to evaluate the performance of self adaptive systems over time on the assumption of presence of impostor’s access.• Assumption: Presence of impostors, enabling the evaluation of impostor’s intrusion over time• Updating Threshold: 0.001 % FAR
  18. 18. •Performance strongly suffers from the operation at stringent threshold •However, the stability can be obtained over time •Different biometrics show difference trend in performance improvementPerformance of self adaptive thumb and indexfingerprint system under the assumption ofimpostor presence
  19. 19. Conclusions and Future work• Self-adaptive systems can result in performance enhancement and classifier’s stability over time• The obtained performance enhancement is very much dependent on the set updating threshold• Different biometric may show different performance trend over time.• The possibility of presence and updating due to impostor is a serious and open issue.• Future work will rely on further development of the conceptual behaviour and significant in-depth analysis of impostor’s effect over time.