Dsip and its biometrics appln
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DSIP and Its applications in Biometrics
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Dsip and its biometrics appln
- 1. Image Processing for Biometrics Preprocessing of Biometric Traits Dr. Vinayak Ashok Bharadi Associate Professor & HOD Information Technology Dept. Thakur College of Engg. & Tech. Kandivali (East), Mumbai -400101
- 2. Physiological Biometric Traits Fingerprint Palmprint Finger Knuckle Prints Face Iris Other examples are Hand Vein, Hand Geometry, Facial Thermogram, Retina, DNA, Ear Geometry, Body Odour.
- 3. Behavioral Biometric Traits Dynamic Signature Keystroke Dynamics Other Examples are Speech, Gait, Facial Emotions 3
- 4. Key Stages in Digital Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 5. Key Stages in Digital Image Processing: Image Aquisition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 6. Key Stages in Digital Image Processing: Image Enhancement Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 7. Key Stages in Digital Image Processing: Image Restoration Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 8. Key Stages in Digital Image Processing: Morphological Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 9. Key Stages in Digital Image Processing: Segmentation Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 10. Key Stages in Digital Image Processing: Object Recognition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 11. Key Stages in Digital Image Processing: Representation & Description Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 12. Key Stages in Digital Image Processing: Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 13. Key Stages in Digital Image Processing: Colour Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 15. Gradient
- 16. Preprocessing • The preprocessing is a multi-step process. Fingerprint Preprocessing Steps are as follows: 1. Smoothening Filter 2. Intensity Normalization 3. Orientation Field Estimation 4. Fingerprint Segmentation 5. Ridge Extraction / Core point Detection 6. Thinning / ROI Extraction 16
- 17. Fingerprint Segmentation Segmentation Process (a) Normalized Input Image (b) Gabor Magnitude Feature Map (c) Segmented Fingerprint (d) Histogram for Gabor Magnitude Feature map (Threshold value is 29) 17
- 18. Fingerprint Segmentation Segmentation Process (a) Normalized Input Image (b) Gabor Magnitude Feature Map (c) Segmented Fingerprint (d) Histogram for Gabor Magnitude Feature map (Threshold value is 29) 18
- 19. Core point Detection The proposed technique is based on multiple features extracted from a fingerprint the feature set includes • Coherence of Grayscale Gradient. • Poincare Index. • Angular Coherence. • Orientation Field Mask. 19
- 20. Core point Detection Contd. (a) Core Point Feature Vectors (b) Selected Fingerprint (c) Fingerprint with Marked Core Point Parameter FS88 Database FVC 2002,2004 Fingerprints with clear Core point Accuracy % 84 68 98 Average Error (Pixels) 5.57 6.13 2.50 Average Execution Time (ms) 500ms 490ms 520ms Core point Detection Test Results 20
- 22. Fingerprint Recognition using Kekre’s Wavelets • Correlation based Fingerprint Recognition is implemented • Kekre’s Wavelets are used for texture feature extraction • Fingerprints are decomposed up to file levels. Wavelet Energy is calculated for each level of decomposition • Relative Energy Entropy & Euclidian Distance is used for classification 22
- 23. Feature Vectors Kekre’s Wavelet Energy Feature Vector Plot (a) Normalized by Total Energy (b) Normalized by Level-wise Energy (a) (b) 23
- 24. FKP ROI Extraction We can see that the Orientation field in (b) is forming a loop surrounding the phalangeal joint which is highlighted by a square. The coherence is also low at the joint area as shown in (c), darker colour indicate low coherence Sum of Angle Difference Cosine 24
- 25. FKP ROI Segmentation Final Feature Map with Horizontal Projection of Feature Map & Vertical Projection of Feature Map, Coordinate system Showing location of X & Y-Axis Coordinate system fitted to the Finger- Knuckle print and corresponding Region of interest Segmented (256X128 Pixels ) 25
- 27. Thank you for your patient listening…