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Image-Based Biometric Person Authentication

Image-Based Biometric Person Authentication. Professor Heikki Kälviäinen Machine Vision and Pattern Recognition Laboratory (MVPR) Department of Information Technology Faculty of Technology Management Lappeenranta University of Technology (LUT) Heikki.Kalviainen@lut.fi

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Image-Based Biometric Person Authentication

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  1. Image-Based Biometric Person Authentication • Professor Heikki Kälviäinen • Machine Vision and Pattern Recognition Laboratory (MVPR) • Department of Information Technology • Faculty of Technology Management • Lappeenranta University of Technology (LUT) • Heikki.Kalviainen@lut.fi • http://www.lut.fi/~kalviai • http://www.it.lut.fi/ip/research/mvpr/ Machine Vision and Pattern Recognition Laboratory

  2. Content • Machine vision and pattern recognition in LUT. • Biometric person authentication. • Face detection. • Why is detection/localization difficult. • Existing approaches. • Proposed algorithm. • Results and evaluation. • New solutions. • Conclusions. Machine Vision and Pattern Recognition Laboratory

  3. Machine Vision and Pattern Recognition Laboratory (MVPR) • Leader: Prof. Heikki Kälviäinen. • 2nd largest computer vision research group in Finland. • Center of Excellence in Research in LUT. • 24 members: • 3 Professors + 3 Post docs + 2 Visiting doctors + 11 PhD students + undergraduate students + industry coordinator. • Co-operation with 14 international universities and research institutes. • Results: 18 Ph.D. degrees (and 3 externally produced), over 400 scientific publications, 40 research projects, and spin-off companies. • Objectives: 2 PhDs/year. • Annual external project funding 700.000 EUR, basic funding 300.000 EUR, total 1.0 million EUR. • http://www.it.lut.fi/ip/research/mvpr/ Machine Vision and Pattern Recognition Laboratory

  4. MVPR Laboratory: Research Profile Machine Vision and Pattern Recognition Laboratory

  5. Machine Vision System Machine Vision and Pattern Recognition Laboratory

  6. BiometricPerson Authentication Hand geometry Fingerprints Face recognition Iris Machine Vision and Pattern Recognition Laboratory

  7. Physiological: Face Fingerprint Iris Retinal scan Ear shape Hand geometry Infrared (face, body parts) Odor Behavioral: Speech Handwriting Signature Lip movements Keystroke dynamics Gait Genetic: Tissue sample Biometric: any measurement of a person’s physiological traits or behavior Machine Vision and Pattern Recognition Laboratory

  8. FACEDETECTImage-Based Biometric Person Authentication http://www.it.lut.fi/project/facedetect/ Docent, Dr. Joni Kamäräinen, Docent, Dr.Ville Kyrki, Mr. Pekka Paalanen, Mr. Jarmo Ilonen, Prof. Heikki Kälviäinen Machine Vision and Pattern Recognition Research Group Lappeenranta University of Technology FINLAND Dr. Miroslav Hamouz, Prof. Josef Kittler, Prof. Jiri Matas Centre for Vision, Speech, and Signal Processing (CVSSP) University of Surrey UNITED KINGDOM Machine Vision and Pattern Recognition Laboratory

  9. Why is Face Detection Difficult? • Object-class recognition (an object to be recognized is not a single entity rather a a group of similar objects). • Faces exhibit significant variability in shape, colour, and texture, and may appear in arbitrary poses: • Appearance variations over the whole population. • Capture effects. • Background. • Illumination. • Video versus still image. Machine Vision and Pattern Recognition Laboratory

  10. State of the Art • Image-based methods: • Scanning window. • Face modeled as manifolds in some high dimensional space. • Moghaddam, Pentland – probabilistic PCA • Sung and Poggio, Rowley et al.- neural networks • Osuna et al. – SVM • Viola and Jones – Adaboost on • Haar features • Jesorsky et al – Haussdorf • distance on edge images Machine Vision and Pattern Recognition Laboratory

  11. State of the Art (cont.) • Feature-based methods: • Face modelled as a viable configuration of local features. • Needs higher resolution than image-based methods. • False alarms.Vogelhuber, Schmid, Gaussian derivatives + angles and length ratios Weber er al., interest operator +statistical model on positionsCristinacce and Cootes, Adaboost + shape model • Warping methods: Variability decomposed into a shape model and the model of local appearance or texture which is iteratively deformed to fit.Cootes et al., Active Shape and Appearance modelsLades et al., Wiskott et al. Dynamic link architectures Machine Vision and Pattern Recognition Laboratory

  12. Introduction • Face verification (authentication)Validating a claimed identity based on the image of a face: are you Mr./Ms. X? • Face recognition (identification)Identifying a person based on an image of his/her face: who are you? • Face detection/localizationLocation of human faces in images at different positions, scales, orientations, and lighting conditions. Machine Vision and Pattern Recognition Laboratory

  13. Proposed Algorithm • Avoiding a scanning window. • Using feature detectors. • Shape-free texture model for the final decision. Machine Vision and Pattern Recognition Laboratory

  14. Feature Detector: 2-D Gabor Filter Machine Vision and Pattern Recognition Laboratory

  15. Gabor Features • Maximal joint localization in the spatial and frequency domain. • Smooth and noise tolerant. • Parameters for invariance manipulation: Frequency Envelope sharpness Orientation Machine Vision and Pattern Recognition Laboratory

  16. columns represent orientations rows represent frequencies image rotation appears as a circular shift of the columns image scaling appears as a shift of the rows (high frequencies may vanish) A SCALE AND ROTATION INVARIANT TREATMENT OF THE RESPONSE MATRIX CAN BE ESTABLISHED, AND THUS, WE CAN CONCENTRATE ONLY HOW TO CLASSIFY THEM IN THE STANDARD POSE Constructing Response Matrix Filter responser(x,y; f,)can be calculated for various frequencies f and orientations  to construct a response matrix. Machine Vision and Pattern Recognition Laboratory

  17. 2-D Gabor Features What do they ”see”? Machine Vision and Pattern Recognition Laboratory

  18. Evidence Extraction Requirements • Scale invariant extraction. • Rotation invariant extraction. • Provides sufficiently small amount of correct candidate points. (n best points from each class; needs confidence measure). Preferred Estimation of evidence scale and orientation. Fast extraction (scalability). Machine Vision and Pattern Recognition Laboratory

  19. eye eye Bayesian classification of features Gaussian mixture model densities (EM estimation) nostril Classifier Construction • Stability property guarantees approximately the Gaussian form of classes in the feature space. • One class may still consist of several sub-clusters (open eye, closed eye, etc.). eye eye nostril Machine Vision and Pattern Recognition Laboratory

  20. 1 2 3 4 5 6 Affine Learned Correspondences Aligned images of objects and manually selected features Variability and correspondences Machine Vision and Pattern Recognition Laboratory

  21. 2 False alarms occur and hypothesis verification is needed 1 3 Instance approved 2 1 1 4 1 5 2 2 2 Affine Hypothesis Search • Evidence extraction. 2. Affine search and match to correspon- dence model. Machine Vision and Pattern Recognition Laboratory

  22. Face Space • Normalization of space where shape variations and capture effects are removed from patterns. • Based on three points on the face -> affine registration. • Optimal with regard to the photometric variance over a big set of faces. Machine Vision and Pattern Recognition Laboratory

  23. Features & Feature Detectors • Features = salient parts of face. • Small localization variance and frequent occurrence over population. • Illumination, scale, rotation, and translation invariance. • Automatic analysis using the face space desirable. Machine Vision and Pattern Recognition Laboratory

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  29. Confidence Regions • Exhaustive search over triplets O(n)n3. • Not all triplets have to verified, regions supporting highly likely transformations can be learned. • Speed-up up to 1 000 times. Machine Vision and Pattern Recognition Laboratory

  30. Performance Measure Strict measure using the location of eye centres, not only an upright bounding box. deye<=0.05 in order to succeed in verification. deye<=0.25 corresponds to the definition of successful detection in the majority of state-of-the-art algorithms. C = ground truth eye center coordinates d = distances between the detected and ground truth ones Machine Vision and Pattern Recognition Laboratory

  31. deye = 0.05 Machine Vision and Pattern Recognition Laboratory

  32. Recognition System Machine Vision and Pattern Recognition Laboratory

  33. BANCA Database • Large realistic face and voice database collected (BANCA database): • 4 languages, each language 6540 images of 52 people. • Three scenarios simulating controlled access, office environment and outdoor scenes. • Publicly available including a rigorous evaluation protocol. Machine Vision and Pattern Recognition Laboratory

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  35. XM2VTS Database Machine Vision and Pattern Recognition Laboratory

  36. BioID Database Machine Vision and Pattern Recognition Laboratory

  37. BANCA Database Machine Vision and Pattern Recognition Laboratory

  38. 3-dimensional Face Recognition • 3-D images. • 3-D algorithms. • Accurate! • Images? • Reference databases? • Speed? Machine Vision and Pattern Recognition Laboratory

  39. FACEDETECT - Publications • Hamouz, M., Kittler, J., Kamarainen, J.-K., Paalanen, P., Kälviäinen, H., Matas, J., Feature-Based Affine-Invariant Localization of Faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 9, 2005, pp. 1490-1495. (Impact factor: 3.810) • Kamarainen, Joni-Kristian, Ville Kyrki, and Heikki Kälviäinen. Invariance properties of Gabor filter based features - Overview and applications. IEEE Transactions on Image Processing, Vol. 15, No. 5, 2006, pp. 1088-1099. (Impact factor: 2.428) • Kyrki, Ville, Joni-Kristian Kamarainen, and Heikki Kälviäinen. Simple Gabor feature space for invariant object recognition. Pattern Recognition Letters, Vol. 25. No. 3. 2004, pp. 311-318. (Impact factor: 1.138) • Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H., Feature Representation and Discrimination Based on Gaussian Mixture Model Probability Densities - Practices and Algorithms, Pattern Recognition, Vol. 39, No. 7, 2006. pp.1346-1358. (Impact factor: 2.153) Machine Vision and Pattern Recognition Laboratory

  40. Conclusions and Future Work • Algorithm successfully tested on a large face authentication data set. • Combination of features brings a significant performance boost. • Gabor jets proved as a suitable local representation of a signal. • Adequate resolution necessary for feature detectors to succeed. • 3-D face recognition much more accurate than 2-D recognition. • Methods for non-frontal poses (more 3-D face research needed). • Speed: real-time solutions (3-D image acquisition and analysis). • Applications: • Security applications: biometric passports, access, cash dispensers, etc. • Surveillance applications. Machine Vision and Pattern Recognition Laboratory

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