dsip and its biometrics appln
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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
Physiological Biometric Traits
FingerprintPalmprint
Finger Knuckle Prints
Face Iris
Other examples are Hand Vein, Hand Geometry, Facial Thermogram, Retina, DNA, Ear Geometry, Body Odour.
Behavioral Biometric Traits
Dynamic Signature
Keystroke Dynamics
Other Examples are Speech, Gait, Facial Emotions
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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
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
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
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
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
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
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
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
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
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
Biometric System Architecture 14
Gradient
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
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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)
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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)
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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.
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Core point Detection Contd.
(a) Core Point Feature Vectors (b) Selected Fingerprint
(c) Fingerprint with Marked Core Point
ParameterFS88
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
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Fingerprint Enrollment 21
Fingerprint Recognition using Kekre’sWavelets
• 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 foreach level of decomposition
• Relative Energy Entropy & Euclidian Distance is used for classification
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Feature Vectors
Kekre’s Wavelet Energy Feature Vector Plot (a) Normalized by Total
Energy (b) Normalized by Level-wise Energy
(a)
(b)
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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
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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 )
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Thank you for your patient listening…