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Fingerprint Recognition Future Directions

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Fingerprint Recognition Future Directions

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    1. Fingerprint Recognition Future Directions Salil Prabhakar Digital Persona Inc.

    2. Fingerprint Applications

    3. Fingerprint Application Functionality

    4. Challenges

    5. Reasons for Accuracy Challenges

    6. Fingerprint Individuality Estimation Accuracy; Information Limitation

    7. Probability of a False Correspondence Accuracy; Information Limitation; Fingerprint Individuality Estimation

    8. Probability of a False Correspondence Accuracy; Information Limitation; Fingerprint Individuality Estimation

    9. Probability of a False Correspondence Accuracy; Information Limitation; Fingerprint Individuality Estimation

    10. Upper Bound on Fingerprint Accuracy Accuracy; Information Limitation; Fingerprint Individuality Estimation

    11. Lower Bound on Fingerprint Accuracy Accuracy; Information Limitation; Fingerprint Individuality Estimation

    12. Information Limitation: Conclusion Accuracy; Information Limitation

    13. Fingerprint Representation Accuracy; Representation Limitation Ideal representation would maximize the inter-class variability and minimize the intra-class variability

    14. Fingerprint Representation Accuracy; Representation Limitation

    15. Conventional Representations Accuracy; Representation Limitation Minutiae-based Sequential design based on the following modules: Segmentation, local ridge orientation estimation (singularity and more detection), local ridge frequency estimation, fingerprint enhancement, minutiae detection, and minutiae filtering and post-processing. Ridge Feature-based Size and shape of fingerprint, number, type, and position of singularities (cores and deltas), spatial relationship and geometrical attributes of the ridge lines, shape features, global and local texture information, sweat pores, fractal features.

    16. Representations: Future Directions Accuracy; Representation Limitation Improvement of current representations through robust and reliable domain-specific image processing techniques such as: Model-based orientation field estimation Robust image enhancement and masking New richer representations Fusion of various representations

    17. Fingerprint Invariance Accuracy; Invariance Limitation Ideal matcher would perfectly model the invariant relationship in different impressions of the same finger

    18. Minutiae Matching Accuracy; Invariance Limitation Given two sets of minutiae points: where x, y, and q are the x-coordinate, y-coordinate, and minutiae direction. No point correspondence is known a priori Nonlinear deformation between point sets Spurious minutiae and missing minutiae Errors in minutiae position and minutiae direction

    19. Matching: Future Directions Accuracy; Invariance Limitation Alignment remains a difficult problem – develop alignment techniques that remain robust under the presence of false features Understand and model fingerprint deformation Fusion of various matchers (based on the same or different representations)

    20. Scale 1:N Identification is a much harder problem (N large) Accuracy Speed Traditionally: classify fingerprint into one of the few (4 or so) predefined fingerprint types Problem: too few distinct bins; uneven natural distribution into these bins; many “ambiguous” fingerprints (17% NIST4 has two labels)

    21. Scale: Future Directions Continuous classification Feature-based indexing (search and retrieval) schemes (e.g., minutiae triplets) Fast matchers Classifier combination

    22. Multiple Biometrics; Fusion A decision (and lower) level fusion of multiple biometrics can improve performance In identification systems, fusion can also improve speed Independence among modalities is key Even combination of correlated modalities can be no worse than the best performing modality alone Best combination scheme would be application dependent

    23. Performance Evaluation Evaluation types: technology, scenario, operational Dependent on composition of the population (occupation, age, demographics, race), the environment, the system operational mode, etc Ideally, characterize the application-independent performance in laboratory and predict technology, scenario, and operational performances Standardization and independent testing Parametric and non-parametric estimation of confidence intervals and database size Parametric and non-parametric and statistical modeling of inter-class and intra-class variations;

    24. Usability, Security, Privacy Biometrics are not secrets and not revocable Encryption, secure system design, and liveness detection solve this problem Unintended functional scope; unintended application scope; covert acquisition Legislation; self-regulation; independent regulatory organizations Biometric Cryptosystems: fingerprint fuzzy vault Alignment Similarity metric in encrypted domain Variable and unordered representation Performance loss; ROC remains the bottleneck

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