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FINGERPRINT

FINGERPRINT. TOPICS COVERED Sensors Used Representations Matching Algorithms State of Art Research Problems. Sensors Used. Basic Types. Optical Sensors Oldest and most widely used Solid State Sensors Thermal Based Sensors Pressure Based Sensors Recent : rarely used

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FINGERPRINT

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  1. FINGERPRINT TOPICS COVERED Sensors Used Representations Matching Algorithms State of Art Research Problems by Amit Mhatre and Roshith Rajagopal

  2. Sensors Used by Amit Mhatre and Roshith Rajagopal

  3. by Amit Mhatre and Roshith Rajagopal

  4. Basic Types • Optical Sensors • Oldest and most widely used • Solid State Sensors • Thermal Based Sensors • Pressure Based Sensors • Recent : rarely used • Ultrasonic Based Sensors • Recent : rarely used by Amit Mhatre and Roshith Rajagopal

  5. Optical Sensors • The finger is placed on a coated plate • Charged Coupled Device (CCD) converts the image of the fingerprint • It also takes a picture of the dirt, greases, and contamination found on the finger by Amit Mhatre and Roshith Rajagopal

  6. Optical Sensors • The process, referred to as ‘Frustrated Total Internal Reflection’ by Amit Mhatre and Roshith Rajagopal

  7. Optical Sensors • Dirty Fingerprints cannot use system effectively • Latent prints are leftover prints from previous users • No ESD issues • Durable to incidental damage by Amit Mhatre and Roshith Rajagopal

  8. Solid State Capacitance Sensors • The sensor uses solid-state capacitance sensing to capture unique fingerprint data • Finger as one plate • Surface of sensor as other plate • Sensor surface - silicon chip containing an array of 90,000 capacitor plates with sensing circuitry at 500-dpi pitch by Amit Mhatre and Roshith Rajagopal

  9. Solid State Capacitance Sensors • Veridicom – one of the leading players • Easy Integration into a variety of electronics by Amit Mhatre and Roshith Rajagopal

  10. Solid State Capacitance Sensors • Very difficult to spoof. • Immune to day-to-day fingerprint variations • Low power • Immune to ambient light • High image quality • Scratch resistant by Amit Mhatre and Roshith Rajagopal

  11. Thermal Based • Infrared to sense the temperature differences between the ridges and valleys of the finger to create a fingerprint image • Temperature differential between the skin ridges and the air caught in the fingerprint valleys • No latent prints • Good Quality Images by Amit Mhatre and Roshith Rajagopal

  12. Thermal Based • Sweeping needs some user skill • High power consumption  to avoid the possibility of a thermal equilibrium between the sensor and the fingerprint surface. • AMTEL – one of the leading players by Amit Mhatre and Roshith Rajagopal

  13. Pressure-Based Sensors • Principle: • when a finger is placed over the sensor area, only the ridges of the Fingerprint come in contact with the sensor piezo array • pressure sensors generate a 1-bit binary image by Amit Mhatre and Roshith Rajagopal

  14. Pressure Based Sensors • Works well with Dry as well as Wet skin • Larger Sensing Area by Amit Mhatre and Roshith Rajagopal

  15. Ultra Sound Based Sensors • Use High Frequency Sound Waves • Transmits acoustic waves and measures the distance based on the impedance of Finger, Plate and Air • Ultrasound can penetrate through many mediums • Considered perhaps the most accurate of the fingerprint technologies by Amit Mhatre and Roshith Rajagopal

  16. Acquisition Problems • Regular Scratches • Skin Peeling due to weather conditions • Natural Permanent creases • Temporary Creases • Dirty Fingers • Long Nails • Ethnic Trait by Amit Mhatre and Roshith Rajagopal

  17. Feature Extraction by Amit Mhatre and Roshith Rajagopal

  18. Fingerprint Features Classification Distinguishing Characteristics by Amit Mhatre and Roshith Rajagopal

  19. Fingerprint Classification • On the basis on ridge flow patterns • Arch, Tented Arch, Whorl and Loop (Right/Left) by Amit Mhatre and Roshith Rajagopal

  20. Distinguishing FeaturesRidge Features and their Position by Amit Mhatre and Roshith Rajagopal

  21. MINUTIAE • Points where ridges terminate, bifurcate or merge with each other are called minutiae points • In law enforcement 12 -16 matching minutiae are sufficient to match a person by Amit Mhatre and Roshith Rajagopal

  22. Image Enhancement • Noise in fingerprint may be due to dry or wet skin, dirt, cut or noise of capture device • Enhancement operations • Adaptive Matched Filter – to enhance ridges oriented in the same direction as those in the same locality • Adaptive Thresholding (binarization) by Amit Mhatre and Roshith Rajagopal

  23. Minutiae Extraction Algorithm by Amit Mhatre and Roshith Rajagopal

  24. Feature Extraction • Original Grey level image • Orientation of the ridges calculated by Fourier transform by Amit Mhatre and Roshith Rajagopal

  25. Feature Extraction (Contd) • Segmentation into foreground and background • Masking out the background is done in order to retrieve the ridges by Amit Mhatre and Roshith Rajagopal

  26. Feature extraction (Contd) • Finally minutiae points are calculated from the ridge image • Endings have 1 adjacent black pixel ( 8 neighborhood ) • Bifurcations have more than 2 adjacent black pixels • Finally the minutiae points are superimposed on the original image by Amit Mhatre and Roshith Rajagopal

  27. Feature extraction (Contd) • Minutiae extracted are represented by - Their (x,y) coordinate - Orientation (Θ) - Forming a 3 tuple (x, y, Θ) - Also the type of minutiae i.e. Ridge ending, ridge bifurcation could be stored. by Amit Mhatre and Roshith Rajagopal

  28. Chain coded Ridge Extraction MethodBy Dr Venugopal, Zhixin Shi & John Schneider by Amit Mhatre and Roshith Rajagopal

  29. Chain coded Ridge Extraction MethodBy Dr Venugopal, Zhixin Shi & John Schneider • Pin – vector leading to candidate point P from several previous neighboring contour points • Similarly Pout • Calculate S(Pin , Pout) < x1y2 – x2y1 • S(Pin , Pout) > 0 Left Turn and S(Pin , Pout) < 0 Right Turn • Threshold by Amit Mhatre and Roshith Rajagopal

  30. Tessellated approach • Equal sized non-overlapping windows over the image and normalizing pixel intensities within the window to constant mean and variance. • Windows of size 30*30 • Bank of 8 Gabbor filters is applied to each window • Absolute average deviation of intensity in each filtered cell is treated as a feature value • Thus 8 Feature values for each cell • Feature values from all cells concatenated inorder to form feature vector of the image. • For a 300 * 300 image – 648d feature vector. by Amit Mhatre and Roshith Rajagopal

  31. Matching Algorithms Fingerprints represented by Minutiae points Simplest Method: “Point Pattern Matching” Requirement: Correspondence between Template and Input No Deformations Every Minutiae Localized Not Realistic by Amit Mhatre and Roshith Rajagopal

  32. Matching Algorithms • Requirement of the Matching Model: • Different Locations • Different Orientations • Different Pressure • Spurious Minutiae • Missing Genuine Minutiae • Linear / Non-linear perturbation of pair of minutiae by Amit Mhatre and Roshith Rajagopal

  33. Matching Algorithms • Different Approaches • Image Based • Graph Based • Ridge Based • Minutiae Based by Amit Mhatre and Roshith Rajagopal

  34. Point Based Matching #1 . Relaxation Method: • Iteratively adjust confidence level • Inherently slow due to Iterative property #2. Hough Transform Method • Detecting Peaks in Transformation parameter Space • If only a few minutiae points, difficult to accumulate enough evidence for a match by Amit Mhatre and Roshith Rajagopal

  35. Point Pattern Matching #3. Energy Minimization Approach • Correspondence between pair of points by using an energy function • Energy function based on initial set of possible correspondences • Very Slow  unsuitable for real-time applns. #4. Tree-pruning Approach • Search over a tree of possible matches • Strict requirements: equal number of points • Impractical requirements by Amit Mhatre and Roshith Rajagopal

  36. Point Pattern Matching • Alignment Based • Alignment Stage • Transformations determined for alignment • Matching Stage • Elastic String Matching Algorithm by Amit Mhatre and Roshith Rajagopal

  37. Alignment Based Matching • ALIGNING • Corresponding point pairs • Exhaustive test • Large Number of tests • Impractical though Feasible • Aligning Minutiae by aligning Ridges by Amit Mhatre and Roshith Rajagopal

  38. Ridge Alignment by Amit Mhatre and Roshith Rajagopal

  39. Post Alignment Matching • Counting the number of overlapping points – if exact overlap • Elastic Algorithm – tolerating deformation • Bounding Box • Minutiae Points as Strings • Dynamic Programming approach for String Matching ( edit distances ) • Distance measure  penalty for a mismatch • Adaptive Bounding Box by Amit Mhatre and Roshith Rajagopal

  40. Ridge Based Matching • Correlation Based  compare the global patterns: Ridge and Furrows • Don’t perform very well due to noisy Images • Invariant Representation needed • Strength of Ridges at various orientations • 2D Gabor wavelets by Amit Mhatre and Roshith Rajagopal

  41. Ridge Based Matching Parameters: f -> Frequency  Ridge Frequency Sx, Sy -> Standard Deviations Theta -> Orientation by Amit Mhatre and Roshith Rajagopal

  42. Ridge Based Matching • Each of 8 Gabor Filters applied • Standard Deviation Map for each of 8 Images • For Alignment, • Weighted Correlation • Euclidean Distance measure by Amit Mhatre and Roshith Rajagopal

  43. Graph Based Matching • Clustering Techniques used • Homogeneous Regions • Regions with similar Direction • Using these regions, develop ‘Relational Graphs’ • invariant with respect to translation and rotation • Tolerates Partial Matches by Amit Mhatre and Roshith Rajagopal

  44. by Amit Mhatre and Roshith Rajagopal

  45. Multilevel Matching • Text Based • Textual Fields: • age range / color of hair and eye • Class Based • 5 classes of Fingerprints • Ridge – Density Based • Count of the ridges • Elastic Matching by Amit Mhatre and Roshith Rajagopal

  46. Performance Evaluation • FVC 2004 Fingerprint Verification Competition • 4 databases – 2 optical, 1 thermal sweeping sensor and 1 synthetic • REJ, FMR, FNMR, ROC, Genuine/Imposter distribution • Enrollment time, Matching time, average and maximum template size, memory allocated by Amit Mhatre and Roshith Rajagopal

  47. Best Algorithm • Winner of FVC2002 – Bioscrypt Inc. • Ridge patterns not ridge endings • Pattern based templates not minutiae based • correlation of ridge patterns • Heavy weights to areas where images are clear and highly complex • Incompatible with minutiae based systems by Amit Mhatre and Roshith Rajagopal

  48. Pressure based Systems • Pressure sensitive • Wet or dry fingers • Captures print of the finger not just image of the print • By Elform OEM Inc. by Amit Mhatre and Roshith Rajagopal

  49. Ultrasonic Fingerprint Technology • Sound waves reflecting off ridges and valleys on the finger • Oblivious to dirt, grease, ink, moisture, grime, or other substances routinely found on fingers which cause the most false readings • Fingerprints of children by Amit Mhatre and Roshith Rajagopal

  50. Ultrasonic Fingerprint Technology by Amit Mhatre and Roshith Rajagopal

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