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Multibiometrics ver5

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Rowshina Nikzad

The document discusses multi-biometric identification using iris and periocular biometrics. It provides an overview of iris and periocular biometrics, including the structure of the eye, advantages of iris recognition, acquisition techniques, preprocessing, segmentation approaches, feature extraction methods, and current research directions. Some key points covered include the use of iris recognition in large-scale ID programs, the subsystems of ocular biometrics like iris, retina, cornea, techniques like hybrid biometrics that combine iris and other biometrics, and segmentation approaches such as using active contours, Hough transforms, graph cuts and cellular automata.

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MULTI-BIOMETRICS: IRIS AND PERIOCULAR INSTRUCTOR: ASSOC.DR.PROF.DR. ÖNSEN TOYGAR ROSHINA NIKZAD 17500231 FALL SEMESTER 2019 1 according to article : Ocular biometrics: A survey of modalities and fusion approaches Ishan Nigam, Mayank Vatsa ,Richa Singh
INDEX:  Introduction  Definition  Place of application  Goals  Reason of Biometric Approach  Subsystem of Ocular Biometric  Hybrid Biometric  Structure of Eyes  Advantage of using Iris for recognition  Acquisition Research  Processing of technique  Iris segmentation Approach  Feature Extraction Methods  Matching and Indexing Methods  Covariates  Periocular  Current research direction 2
Biometrics refers to: the use of physiological and behavioral characteristics of humans for establishing their identity. Among physiological characteristics, several body parts have been studied that demonstrate biometric properties such Biometrics CollectabilityPermanenceuniquenessuniversal 3
• Biometrics, an integral component of Identity Science, is widely used in several large-scale-county- wide projects to provide a meaningful way of recognizing individuals. • Iris recognition is used in Unique Identification Authority of India’s Aadhaar Program • the United Arab Emirate’s border security program Place Of Application 4
Need to identify new Methods Weakness in traditional ways Alternate our existing Methods Reasons for the biometrics approach 5
cornea Lens optic nerve Retina Pupil iris the periocular region The field of ocular biometrics consists of several subsystems such as : 6
HYBRID BIOMETRICS: For example, using one person's fingerprints and iris characteristics simultaneously 7
STRUCTURE OF EYES 8
ADVANTAGES OF USING IRIS FOR RECOGNITION  The iris is an internal organ  It is well protected against injury .  Iris geometry is flat  Iris scanning is very simple  Iris Scan can be done from a distance of 10 cm without touching the device.  Many genetic diagnoses like DNA depend on the iris.  The matching results are absolute  there is no potential match for the iris patterns, as there is no user intervention in setting the threshold or system sensitivity.  Iris identification is possible even when a small portion of the entire eye is visible  each iris pattern will contain much more information than the sum of the data collected from a finger, a face and a hand. 9
THE SEQUENCE OF AUTHENTICATION STEPS WITH THE IRIS Acquisition Preprocessing Segmentation Feature Extraction Matching 10
IRIS ACQUISITION TECHNIQUES: 11 •Proposed NIR imaging (750–950 nm) for iris recognitionDaugman •Proposed apparatus that acquires images at 1.5 m from subject. • Face detection is used to localize user’s face. •Tracks face and segments iris region Venugopalan & Savvides •Images captured using computational photography flutter-shutter technique. •Deconvolution is used to estimate sharp image from captured imageMcCloskey et al. •Hardware constitutes Commercial Off The Shelf (COTS) components. •Incorporate velocity estimation and focus tracking modules. •Subject’s speed is estimated and used to tune focus of system Venugopalan et al. •Compare three commercially available iris sensors. •Performance of each sensor is analyzed. • Experiments are performed to investigate how external factors affect acquisition Connaughton et al. •Capture video sequence of ocular region at multiple focal lengths. •Fuse frames to yield single image. •Combination of focus bracketing and lateral white LED lighting is used Tankasala et al. • System captures high quality Near Infrared videos. •Significant improvements in recognition on increasing images are reportedBoehnen et al. •[Acquisition] Dilation-aware iris enrollment scheme; •shows that optimal dilation is near the median or mean if relationship between match scores and dilation is linear Ortiz et al.
IRIS PREPROCESSING TECHNIQUES: Liu et al. •Image is classified as either defocused or motion blurred. • PSF is refined based on gradient maps and noise model. •Image deconvolution is performed Ortiz and Bowyer •Implement dilation-aware enrolment phase to choose image based on empirical dilation ratio distribution Li and Savvides •GMMs used to model probabilistic distributions of valid and invalid regions on iris images. •Simulated Annealing technique is applied to optimize parameters Sgroi et al. •Propose diffused illumination system. •Matching algorithms are used to study diffused image templates Tan and Kumar •Noise treated as inconsistent fragile bits. •Model relationship between iris codes and noise. •Features are extracted using 1D log Gabor filters 12
13 IRIS SEGMENTATION APPROACHES: Tan et al. Clustering based coarse iris localization. Localization of pupillary and limbic boundaries and localization of eyelids is performedZhang et al. Robust gradient map is used for iris localization. SIMC generated using spatial information and coarse iris location. Segmentation achieved by level set method Roy et al. Game-theoretic decision making procedure to segment irises. Integrates region based segmentation and gradient based boundary localization Pundlik et al. Image is modeled as MRF. Energy minimization is achieved via graph cuts. Model iris as ellipse to refine segmentation Zuo and Schmid combined scheme for pre-processing, pupil segmentation, iris segmentation, and occlusion detection is reported De Marisco et al. Pre-process using posterization filter. Canny filtering is applied to locate pupil boundary. Image is transformed to polar coordinates to identify boundary between iris and sclera Proença UBIRIS v2 Sclera and iris are segmented and classified. Polynomial fitting is applied Koh et al. Center of pupil is estimated based on histograms. Pupillary boundary is computed using Hough transform. Apply Hough transform again to localize limbic boundary Du et al. Method incorporates quality filter to eliminate non-valid images. Employs coarse-to-fine segmentation scheme and window gradient based method to remove noiseTan and Kumar Iris features extracted using localized Zernike moments and sclera features are extracted using color features. A robust approach is proposed for post-processing classified iris pixels
14 IRIS SEGMENTATION APPROACHES: Tan and Kumar Multiple higher order local pixel dependencies are used to robustly classify eye region pixels into iris or non-iris regions. Post-processing operations effectively tackle noisy pixels Sutra et al. Pre-processing is performed using anisotropic diffusion. Gradients are computed using Sobel filter and Viterbi algorithm is applied to find contoursLi et al. Locate edge points on iris boundary. Boundary detectors for pupillary, limbic, eyelid boundaries are learned and iris boundaries are localized. Eyelid edge points are modeled as parabolasFernandez et al. Based on energy minimization of one-directional graphs. Pupil localization is achieved by sliding average pattern, model fitting, and defocusing of irrelevant regionsTan and Kumar Iris segmentation approach based on cellular automata using grow-cut algorithm is proposed. Reduces computational complexity while increasing recognition performance Uhl and Wild ND-IRIS-0405 Adaptive Hough transform estimates iris center. Polar transform detects first elliptic pupillary boundary. Ellipsopolar transform is used to find second boundary Li et al. Assembled pupillary contour segments are fitted as an ellipse. Limbic boundary points detected by LBD. Unseen boundary points are extrapolated in eyelid occluded regionsAlonso-Fernandez and Bigun Pupil boundary is searched for and sclera is detected. Eyelid occlusion is computed and the iris is localized Alonso-Fernandez and Bigun Study local and global quality measures for iris segmentation performance. Explore correlation between factors affecting segmentation and matching Tan and Kumar Image is segmented using random walker algorithm.
SUMMARY OF IRIS SEGMENTATION APPROACHES(COUNT2) 15
16 Sunder and Ross [65] Investigate macro-features (moles, freckles, nevi, melanoma) as soft biometric traits. SIFT descriptor is used to represent the macro-features Zhou and Kumar [67] LRT exploits the orientation information from the local features. Dominant orientation is used to generate feature representation. Similarity is computed using matching distances Scotti and Piuri [68] RST features are extracted. Inductive classifier segments iris Hosseini et al. [69] Shape features are extracted from pigment melanin in visible light Roy et al. [70] Non-ideal Active contour model is deployed to segment non-ideal iris. A Modified Contribution- Selection Algorithm selects informative features without affecting recognition performance Hollingsworth et al. [71] Improve recognition by masking fragile bits. Fragile Bit Distance is established to measure coincidence of fragile bit patterns Zhang et al. [73] DAISY descriptors are extracted from iris. Iris key points are localized on feature map. Extracted key points are matched IRIS FEATURE EXTRACTION TECHNIQUES:
17 Proença and Santos [75] Segment iris into coherent regions. Color and shape information is extracted. Perform fusion with prior state- of-theart approaches Kumar et al. [76] Recognition of distantly acquired irises using LRT based orientation features. Iris is modeled as sparse coding solution based on computationally efficient LRT dictionary Li and Wu [77] Iris boundaries and eyelids are localized. Log-Euclidean Co-variance Matrices are used to model correlation of spatial coordinates, intensities, 1st and 2nd-order image derivatives Rahulkar and Holambe [78] IITD Iris Features are extracted based on Triplet Half-Band Filter Bank. Post-classifier system achieves robustness against intra-class iris variations Zhang et al. [79] Propose Perturbation-enhanced Feature Correlation Filter for robust iris matching. Correlation filters are utilized for Gabor images matching da Costa and Gonzaga [80] Capture information about manner in which eye reacts to light. Allows the validation of attributes such as to check if input image being analyzed is from a living iris Liu and Li [81] Normalized iris image is divided into patches, represented by SIFT descriptors. The low- dimensional features are encoded to binary codes. Matching is performed by counting binary codes in agreement IRIS FEATURE EXTRACTION TECHNIQUES:
18 Kumar and Chan [82] Hyper-complex sparse representation is used. Orientation of iris texture is extracted using dictionary of oriented atoms. Iris representation as quaternionic sparse coding problem is solved using convex optimization strategy Zhang et al. [83] Color Texton is combined with pixel value in multiple color spaces. The image is represented by histogram of the learnt Texton vocabulary Wang et al. [84] Large margin loss function is adopted to learn robust model. Information from each feature is considered to remove noise. The model is solved using Simplex algorithm.Nguyen et al. [85] MBGC Feature-level super- resolution in non-linear Gabor feature domain is performed. Compared to classic pixel-level super-resolution approaches Zhang et al. [86] Extract key-point features from bandpass component of iris images. Extract ordinal features from lowpass component and perform match-score fusion Sun et al. [87] Hierarchical Visual Codebook integrates Vocabulary Tree and Locality-constrained Linear Coding. Adopts coarse-tofine visual coding strategy IRIS FEATURE EXTRACTION TECHNIQUES:
19 Rathgeb et al. Reorder bits, and dynamically reject high Hamming Distance score candidates Gadde et al. Normalized image is divided into vertical segments. Based on occurrence of N-bit pattern among segments, iris is assigned index value based on segment number using Burrows- Wheeler Transform Vandal and Savvides Not mentioned. Parallel implementation of template matching with embedded rotational invariance on CUDA architecture is proposed Proença Iris is regarded as a pattern, which is a set of simpler sub-patterns. Match occurs if pattern representation is isomorphic with a pattern stored in gallery Dong et al. Personalized iris matching strategy using class-specific weight map is learned from training images of an iris class. Appropriate weight is assigned to each feature code for matching Farouk Circular Hough transform is used for segmentation. Elastic Graph Matching based similarity function is used to perform recognition Gyaourova and Ross Generate fixed-length codes. Index code is constructed by computing match scores between probe and a set of reference images. Candidate identities are retrieved based on the similarity between index codes MATCHING METHODS 1
20 Dey and Samanta Iris Gabor energy features are calculated from iris texture at different scales and orientations to generate index key. Index space is created based on values of index keys of all gallery subjects Tsai et al. Non-linear normalization model provides accurate iris positioning. Segmentation method refines detected inner and Ordinal measures, color analysis are adopted for iris matching. Textons, semantic information are used for eye matching. Matching scores obtained are used for score-level fusion Wang et al Iris is segmented and normalized. Features are constructed based on 2D-Gabor transform. Two independent AdaBoost Santos and Hoyle Wavelet based features are extracted from iris. Local descriptors are extracted from periocular region. Logistic regression is applied to outputs of the two approaches Shin et al. Identify eye as ‘‘left or right’’. Separability between classes is increased.1D Gabor filter is applied to individual color Li et al. Weighted Co- occurrence Phase Histogram represents local characteristics of texture patterns. Weighting function allows every pixel’s phase angle to affect several histogram bins MATCHING METHODS II
21 De Marisco et al. Combines two local feature descriptors, Linear Binary Patterns, and discriminable textons. Individual methods are combined using weighted mean scheme at score level Li and Ma Random Sample Consensus is used for localization of iris boundaries. Image registration method is applied. A subset of Gabor filters is used to enhance output Szewczyk et al. Image is preprocessed and occluding artifacts are eliminated. 324-bit template is obtained using reverse biorthogonal wavelet. Similarity score is used for matching Proença Gallery iris codes are decomposed at multiple scales. Position in N-ary tree is determined. Distances between multi-scale centroids is used to penalize paths in tree for matching Liu et al. Statistical relationship between binary code of LR iris image and binary code of latent HR iris image is established based on a Markov network Tomeo-Reyes and Chandran Explore multi-part fusion schemes, multi- sample fusion schemes, and integration of these two schemes. Effectiveness is evaluated under miosis and mydriasis Liu et al. Ideal pairwise similarities are defined on training set. Mahalanobis distance is learnt by minimizing divergence between matching results and ideal results MATCHING METHODS III
COVARIATES: Off-angle iris recognition Iris recognition in spoofing scenarios Unconstrained Iris recognition Iris recognition in presence of template-aging Cross-spectral iris recognition Cross-Sensor iris recognition Other iris recognition technique 22
23 Off-Angle Circle rectification converts non-orthogonal iris to approximately orthogonal. Edge-type multi-scale maps characterize iris pattern. Matching is performed using an ensemble of weak classifiers Biorthogonal Wavelet Networks (BWN) are trained. Non-ideal factors are adjusted by repositioning BWN. Synthetic irises are generated. Tests are performed on real and synthetic Human eye model and ray-tracing techniques are used to compute transformation function that reconstructs the off-angle iris to its frontal, non- refracted state Predict orientation of the iris and apply orientation-specific template for preprocessing to perform classification
24 Study effect of soft lenses and gas-permeable lenses towards degradation of recognition performance. Use commercial off-the shelf recognition systems Venugopalan and Attempt to bypass recognition system by generating alternate iris texture patterns. Discriminatory features are identified in the spoofed iris and embedded into the spoofing iris’ texture Investigate effect of alcohol consumption. The pre-consumption and post-consumption overlap between genuine and impostor match scores increases by approximately 20% Investigate the role of contact lenses in obfuscation of iris texture and analyze the effect of contact lenses on the performance of iris recognition Investigate iris recognition with respect to print spoofing attacks. Plausibility of identity obfuscation and identity impersonation is established Contact Lens Detection dataset Address unseen lens patterns present in iris recognition. Introduce Binarized Statistical Image Features to capture difference in textural information between images rcle rectification converts non-orthogonal iris to approximately orthogonal. Edge-type multi-scale maps characterize iris pattern. Matching is performed using an ensemble of weak classifiers Biorthogonal Wavelet Networks (BWN) are trained. Non-ideal factors are adjusted by repositioning BWN. Synthetic irises are generated. Tests are performed on real and synthetic Human eye model and ray- tracing techniques are used to compute transformation function that reconstructs the off-angle iris to its frontal, non-refracted state Predict
25 Unconstrained Propose quality measure that handles segmentation errors and alignment variations. Treats different regions separately and combines results depending on the quality of the region Algorithm is proposed for segmentation that can handle variable resolutions, illumination, and occlusion
26 Template Aging Investigate change in irides with time. Feature extraction is performed using 1D log-Gabor filter. Obtain information by decomposition of iris into complex-valued phase coefficients Iris template aging study over a time-lapse of 2 years. Investigates degree to which template aging effect is related to pupil dilation and contact lenses Investigate factors which contribute towards template aging. Report change in pupil dilation, changes to enrollment and matching as significant factors ND Study whether fall in iris recognition performance occurs due to age or covariates such as poor acquisition, presence of occlusion, noise, and blur Study aging effects developed in digital image sensors over time. Propose method to investigate sensor aging by simulative ageing of iris images
27 Cross-Spectral Non-linear adaptive model is proposed to use visible range iris to predict value of corresponding NIR iris. Predicted value is compared with real NIR iris using log-Gabor filter Most suitable wavelength for iris recognition is found based on amount of available texture information and matching performance
28 Cross-Sensor Use three commercial iris sensors and three iris matching systems to investigate impact of cross-sensor matching on system performance in comparison to single-sensor performance Features are extracted and classification model for iris sensor is applied. Used to classify probe into a camera class. Selective enhancement algorithm is applied for respective sensor Objective function minimizes misclassification error and achieves sparsity in coupled feature spaces. Employs regularization model based on half-quadratic optimization Propose framework to learn transformations on iris biometrics. Framework is applied to reduce distance between intra-class samples and increase distance between inter-class samples
PERIOCULAR: Periocular • Verification and identification using periocular region • Soft biometrics • Human Performance Evaluation 29
CURRENT RESEARCH DIRECTIONS IN PERIOCULAR BIOMETRICS: Current research directions in periocular biometrics: • Cross –Spectral periocular recognition • Anti-Spoofing measures • Unconstrained recognition at a distance 30
RETINA BIOMETRIC: 31
EMERGING OCULAR BIOMETRICS : 32
33 -Proposed periocular biometric trait. -HOG, LBP, SIFT features are extracted. LBP and SIFT are fused to achieve optimal recognition - Effect of pose variation, occlusion, template aging is studied. Score-level fusion of left and right periocular region using SIFT, HOG, LBP is applied circular LBP features for periocular recognition in visible spectrum are proposed. Normalized score-level fusion applied. Weighted sum-rule score fusion applied to left and right periocular regions Binary Pattern is fused with Kernel Correlation Feature Analysis. Recognition using fusion of Discrete Wavelet Transform and LBP MBGC LBP captures texture and color histograms are extracted followed by scorelevel fusion. Information from left and right periocular regions is fused at score-level PERICULAR RECOGNITION I
34 FRGC Study effect of illumination & resolution change, blurring. Highest performance degradation observed for blurring. Study conducted across sessions Demonstrate performance of LBP. Suggest that skin texture, eye folds, periocular contours adequate for verification Present genetic based Type II feature extraction for LBP. Feature optimization achieved and significant improvement in recognition accuracy reported FRGC, MBGC Score-level fusion of periocular skin texture (LBP) and color (histograms) information. Establishes that periocular appearance is unique for each eye Studies performance variation for scale, pose, occlusion, pigmentation variation. Non- linear score-level fusion applied to LBP, SIFT, HOG descriptors PERICULAR RECOGNITION II
35 Present periocular acquisition system using PTZ camera. Walsh-Hadamard transform encoded local binary pattern applied to extract information and Non-ideal recognition: illumination variation using homomorphic filters and Self-Quotient images; pose change through geometric transformations Study effectiveness of Globally Coherent Elastic Graph Matching to compensate distortions due to expressions Apply subspace representations on Discrete Transform encoded LBP. Periocular performance is at par with face recognition. Gains tolerance to occlusion Neural networks train classifiers on cross-spectral images using Pyramid of HOG. Score-level sum rule fusion applied to NIR left and right eye images PERICULAR RECOGNITION III
FUSING OCULAR INFORMATION: Fusing ocular information Intra – ocular Fusion Fusion of ocular traits with other biometric modalities 36
INTRA–OCULAR FUSION APPROACHES : 37
INTRA–OCULAR FUSION APPROACHES : 38
FUSION OF OCULAR TRAITS WITH OTHER BIOMETRIC MODALITIES: 39
FUSION OF OCULAR TRAITS WITH OTHER BIOMETRIC MODALITIES(COUNT1): 40
DATASETS AND SOFTWARE: Datasets and Software: Datasets CASIA Iris Datasets Notre Dame and NIST Datasets UBIRIS and NICE Datasets The VARIA Retina Database Face Datasets used for Ocular Recognition Multimodal databases that include ocular modalities Open source and commercial soft wares Libor Madek’s MATLAB Source Code for Biometric Identification system based on Iris Pattern The Video Based Automatic Systems For Iris Recognition VeriEye The Eye movement classification soft ware software EFDGVSv 41
OCULAR BIOMETRICS’ DATASETS: 42
OCULAR BIOMETRICS’ DATASETS(COUNT1): 43
CONCLUSION: (PATH FORWARD) Improved sensing technology Exploration of advanced machine learning algorithms for better representation and classification algorithms Heterogeneous recognition Ocular recognition at a distance Multimodal ocular biometrics Benchmarking standards and open – source software 44
45 CONCLUSION: (REVIEW) In this presentation, these topics have been the review: ocular modalities such as iris and periocular is presented. Information fusion approaches that combine ocular modalities with other modalities are reviewed. Future research directions are presented on sensing technologies, algorithms, and fusion approaches.
THE END Any Questions? Thanks for your attention 46

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Multibiometrics ver5

  • 1. MULTI-BIOMETRICS: IRIS AND PERIOCULAR INSTRUCTOR: ASSOC.DR.PROF.DR. ÖNSEN TOYGAR ROSHINA NIKZAD 17500231 FALL SEMESTER 2019 1 according to article : Ocular biometrics: A survey of modalities and fusion approaches Ishan Nigam, Mayank Vatsa ,Richa Singh
  • 2. INDEX:  Introduction  Definition  Place of application  Goals  Reason of Biometric Approach  Subsystem of Ocular Biometric  Hybrid Biometric  Structure of Eyes  Advantage of using Iris for recognition  Acquisition Research  Processing of technique  Iris segmentation Approach  Feature Extraction Methods  Matching and Indexing Methods  Covariates  Periocular  Current research direction 2
  • 3. Biometrics refers to: the use of physiological and behavioral characteristics of humans for establishing their identity. Among physiological characteristics, several body parts have been studied that demonstrate biometric properties such Biometrics CollectabilityPermanenceuniquenessuniversal 3
  • 4. • Biometrics, an integral component of Identity Science, is widely used in several large-scale-county- wide projects to provide a meaningful way of recognizing individuals. • Iris recognition is used in Unique Identification Authority of India’s Aadhaar Program • the United Arab Emirate’s border security program Place Of Application 4
  • 5. Need to identify new Methods Weakness in traditional ways Alternate our existing Methods Reasons for the biometrics approach 5
  • 6. cornea Lens optic nerve Retina Pupil iris the periocular region The field of ocular biometrics consists of several subsystems such as : 6
  • 7. HYBRID BIOMETRICS: For example, using one person's fingerprints and iris characteristics simultaneously 7
  • 9. ADVANTAGES OF USING IRIS FOR RECOGNITION  The iris is an internal organ  It is well protected against injury .  Iris geometry is flat  Iris scanning is very simple  Iris Scan can be done from a distance of 10 cm without touching the device.  Many genetic diagnoses like DNA depend on the iris.  The matching results are absolute  there is no potential match for the iris patterns, as there is no user intervention in setting the threshold or system sensitivity.  Iris identification is possible even when a small portion of the entire eye is visible  each iris pattern will contain much more information than the sum of the data collected from a finger, a face and a hand. 9
  • 10. THE SEQUENCE OF AUTHENTICATION STEPS WITH THE IRIS Acquisition Preprocessing Segmentation Feature Extraction Matching 10
  • 11. IRIS ACQUISITION TECHNIQUES: 11 •Proposed NIR imaging (750–950 nm) for iris recognitionDaugman •Proposed apparatus that acquires images at 1.5 m from subject. • Face detection is used to localize user’s face. •Tracks face and segments iris region Venugopalan & Savvides •Images captured using computational photography flutter-shutter technique. •Deconvolution is used to estimate sharp image from captured imageMcCloskey et al. •Hardware constitutes Commercial Off The Shelf (COTS) components. •Incorporate velocity estimation and focus tracking modules. •Subject’s speed is estimated and used to tune focus of system Venugopalan et al. •Compare three commercially available iris sensors. •Performance of each sensor is analyzed. • Experiments are performed to investigate how external factors affect acquisition Connaughton et al. •Capture video sequence of ocular region at multiple focal lengths. •Fuse frames to yield single image. •Combination of focus bracketing and lateral white LED lighting is used Tankasala et al. • System captures high quality Near Infrared videos. •Significant improvements in recognition on increasing images are reportedBoehnen et al. •[Acquisition] Dilation-aware iris enrollment scheme; •shows that optimal dilation is near the median or mean if relationship between match scores and dilation is linear Ortiz et al.
  • 12. IRIS PREPROCESSING TECHNIQUES: Liu et al. •Image is classified as either defocused or motion blurred. • PSF is refined based on gradient maps and noise model. •Image deconvolution is performed Ortiz and Bowyer •Implement dilation-aware enrolment phase to choose image based on empirical dilation ratio distribution Li and Savvides •GMMs used to model probabilistic distributions of valid and invalid regions on iris images. •Simulated Annealing technique is applied to optimize parameters Sgroi et al. •Propose diffused illumination system. •Matching algorithms are used to study diffused image templates Tan and Kumar •Noise treated as inconsistent fragile bits. •Model relationship between iris codes and noise. •Features are extracted using 1D log Gabor filters 12
  • 13. 13 IRIS SEGMENTATION APPROACHES: Tan et al. Clustering based coarse iris localization. Localization of pupillary and limbic boundaries and localization of eyelids is performedZhang et al. Robust gradient map is used for iris localization. SIMC generated using spatial information and coarse iris location. Segmentation achieved by level set method Roy et al. Game-theoretic decision making procedure to segment irises. Integrates region based segmentation and gradient based boundary localization Pundlik et al. Image is modeled as MRF. Energy minimization is achieved via graph cuts. Model iris as ellipse to refine segmentation Zuo and Schmid combined scheme for pre-processing, pupil segmentation, iris segmentation, and occlusion detection is reported De Marisco et al. Pre-process using posterization filter. Canny filtering is applied to locate pupil boundary. Image is transformed to polar coordinates to identify boundary between iris and sclera Proença UBIRIS v2 Sclera and iris are segmented and classified. Polynomial fitting is applied Koh et al. Center of pupil is estimated based on histograms. Pupillary boundary is computed using Hough transform. Apply Hough transform again to localize limbic boundary Du et al. Method incorporates quality filter to eliminate non-valid images. Employs coarse-to-fine segmentation scheme and window gradient based method to remove noiseTan and Kumar Iris features extracted using localized Zernike moments and sclera features are extracted using color features. A robust approach is proposed for post-processing classified iris pixels
  • 14. 14 IRIS SEGMENTATION APPROACHES: Tan and Kumar Multiple higher order local pixel dependencies are used to robustly classify eye region pixels into iris or non-iris regions. Post-processing operations effectively tackle noisy pixels Sutra et al. Pre-processing is performed using anisotropic diffusion. Gradients are computed using Sobel filter and Viterbi algorithm is applied to find contoursLi et al. Locate edge points on iris boundary. Boundary detectors for pupillary, limbic, eyelid boundaries are learned and iris boundaries are localized. Eyelid edge points are modeled as parabolasFernandez et al. Based on energy minimization of one-directional graphs. Pupil localization is achieved by sliding average pattern, model fitting, and defocusing of irrelevant regionsTan and Kumar Iris segmentation approach based on cellular automata using grow-cut algorithm is proposed. Reduces computational complexity while increasing recognition performance Uhl and Wild ND-IRIS-0405 Adaptive Hough transform estimates iris center. Polar transform detects first elliptic pupillary boundary. Ellipsopolar transform is used to find second boundary Li et al. Assembled pupillary contour segments are fitted as an ellipse. Limbic boundary points detected by LBD. Unseen boundary points are extrapolated in eyelid occluded regionsAlonso-Fernandez and Bigun Pupil boundary is searched for and sclera is detected. Eyelid occlusion is computed and the iris is localized Alonso-Fernandez and Bigun Study local and global quality measures for iris segmentation performance. Explore correlation between factors affecting segmentation and matching Tan and Kumar Image is segmented using random walker algorithm.
  • 15. SUMMARY OF IRIS SEGMENTATION APPROACHES(COUNT2) 15
  • 16. 16 Sunder and Ross [65] Investigate macro-features (moles, freckles, nevi, melanoma) as soft biometric traits. SIFT descriptor is used to represent the macro-features Zhou and Kumar [67] LRT exploits the orientation information from the local features. Dominant orientation is used to generate feature representation. Similarity is computed using matching distances Scotti and Piuri [68] RST features are extracted. Inductive classifier segments iris Hosseini et al. [69] Shape features are extracted from pigment melanin in visible light Roy et al. [70] Non-ideal Active contour model is deployed to segment non-ideal iris. A Modified Contribution- Selection Algorithm selects informative features without affecting recognition performance Hollingsworth et al. [71] Improve recognition by masking fragile bits. Fragile Bit Distance is established to measure coincidence of fragile bit patterns Zhang et al. [73] DAISY descriptors are extracted from iris. Iris key points are localized on feature map. Extracted key points are matched IRIS FEATURE EXTRACTION TECHNIQUES:
  • 17. 17 Proença and Santos [75] Segment iris into coherent regions. Color and shape information is extracted. Perform fusion with prior state- of-theart approaches Kumar et al. [76] Recognition of distantly acquired irises using LRT based orientation features. Iris is modeled as sparse coding solution based on computationally efficient LRT dictionary Li and Wu [77] Iris boundaries and eyelids are localized. Log-Euclidean Co-variance Matrices are used to model correlation of spatial coordinates, intensities, 1st and 2nd-order image derivatives Rahulkar and Holambe [78] IITD Iris Features are extracted based on Triplet Half-Band Filter Bank. Post-classifier system achieves robustness against intra-class iris variations Zhang et al. [79] Propose Perturbation-enhanced Feature Correlation Filter for robust iris matching. Correlation filters are utilized for Gabor images matching da Costa and Gonzaga [80] Capture information about manner in which eye reacts to light. Allows the validation of attributes such as to check if input image being analyzed is from a living iris Liu and Li [81] Normalized iris image is divided into patches, represented by SIFT descriptors. The low- dimensional features are encoded to binary codes. Matching is performed by counting binary codes in agreement IRIS FEATURE EXTRACTION TECHNIQUES:
  • 18. 18 Kumar and Chan [82] Hyper-complex sparse representation is used. Orientation of iris texture is extracted using dictionary of oriented atoms. Iris representation as quaternionic sparse coding problem is solved using convex optimization strategy Zhang et al. [83] Color Texton is combined with pixel value in multiple color spaces. The image is represented by histogram of the learnt Texton vocabulary Wang et al. [84] Large margin loss function is adopted to learn robust model. Information from each feature is considered to remove noise. The model is solved using Simplex algorithm.Nguyen et al. [85] MBGC Feature-level super- resolution in non-linear Gabor feature domain is performed. Compared to classic pixel-level super-resolution approaches Zhang et al. [86] Extract key-point features from bandpass component of iris images. Extract ordinal features from lowpass component and perform match-score fusion Sun et al. [87] Hierarchical Visual Codebook integrates Vocabulary Tree and Locality-constrained Linear Coding. Adopts coarse-tofine visual coding strategy IRIS FEATURE EXTRACTION TECHNIQUES:
  • 19. 19 Rathgeb et al. Reorder bits, and dynamically reject high Hamming Distance score candidates Gadde et al. Normalized image is divided into vertical segments. Based on occurrence of N-bit pattern among segments, iris is assigned index value based on segment number using Burrows- Wheeler Transform Vandal and Savvides Not mentioned. Parallel implementation of template matching with embedded rotational invariance on CUDA architecture is proposed Proença Iris is regarded as a pattern, which is a set of simpler sub-patterns. Match occurs if pattern representation is isomorphic with a pattern stored in gallery Dong et al. Personalized iris matching strategy using class-specific weight map is learned from training images of an iris class. Appropriate weight is assigned to each feature code for matching Farouk Circular Hough transform is used for segmentation. Elastic Graph Matching based similarity function is used to perform recognition Gyaourova and Ross Generate fixed-length codes. Index code is constructed by computing match scores between probe and a set of reference images. Candidate identities are retrieved based on the similarity between index codes MATCHING METHODS 1
  • 20. 20 Dey and Samanta Iris Gabor energy features are calculated from iris texture at different scales and orientations to generate index key. Index space is created based on values of index keys of all gallery subjects Tsai et al. Non-linear normalization model provides accurate iris positioning. Segmentation method refines detected inner and Ordinal measures, color analysis are adopted for iris matching. Textons, semantic information are used for eye matching. Matching scores obtained are used for score-level fusion Wang et al Iris is segmented and normalized. Features are constructed based on 2D-Gabor transform. Two independent AdaBoost Santos and Hoyle Wavelet based features are extracted from iris. Local descriptors are extracted from periocular region. Logistic regression is applied to outputs of the two approaches Shin et al. Identify eye as ‘‘left or right’’. Separability between classes is increased.1D Gabor filter is applied to individual color Li et al. Weighted Co- occurrence Phase Histogram represents local characteristics of texture patterns. Weighting function allows every pixel’s phase angle to affect several histogram bins MATCHING METHODS II
  • 21. 21 De Marisco et al. Combines two local feature descriptors, Linear Binary Patterns, and discriminable textons. Individual methods are combined using weighted mean scheme at score level Li and Ma Random Sample Consensus is used for localization of iris boundaries. Image registration method is applied. A subset of Gabor filters is used to enhance output Szewczyk et al. Image is preprocessed and occluding artifacts are eliminated. 324-bit template is obtained using reverse biorthogonal wavelet. Similarity score is used for matching Proença Gallery iris codes are decomposed at multiple scales. Position in N-ary tree is determined. Distances between multi-scale centroids is used to penalize paths in tree for matching Liu et al. Statistical relationship between binary code of LR iris image and binary code of latent HR iris image is established based on a Markov network Tomeo-Reyes and Chandran Explore multi-part fusion schemes, multi- sample fusion schemes, and integration of these two schemes. Effectiveness is evaluated under miosis and mydriasis Liu et al. Ideal pairwise similarities are defined on training set. Mahalanobis distance is learnt by minimizing divergence between matching results and ideal results MATCHING METHODS III
  • 22. COVARIATES: Off-angle iris recognition Iris recognition in spoofing scenarios Unconstrained Iris recognition Iris recognition in presence of template-aging Cross-spectral iris recognition Cross-Sensor iris recognition Other iris recognition technique 22
  • 23. 23 Off-Angle Circle rectification converts non-orthogonal iris to approximately orthogonal. Edge-type multi-scale maps characterize iris pattern. Matching is performed using an ensemble of weak classifiers Biorthogonal Wavelet Networks (BWN) are trained. Non-ideal factors are adjusted by repositioning BWN. Synthetic irises are generated. Tests are performed on real and synthetic Human eye model and ray-tracing techniques are used to compute transformation function that reconstructs the off-angle iris to its frontal, non- refracted state Predict orientation of the iris and apply orientation-specific template for preprocessing to perform classification
  • 24. 24 Study effect of soft lenses and gas-permeable lenses towards degradation of recognition performance. Use commercial off-the shelf recognition systems Venugopalan and Attempt to bypass recognition system by generating alternate iris texture patterns. Discriminatory features are identified in the spoofed iris and embedded into the spoofing iris’ texture Investigate effect of alcohol consumption. The pre-consumption and post-consumption overlap between genuine and impostor match scores increases by approximately 20% Investigate the role of contact lenses in obfuscation of iris texture and analyze the effect of contact lenses on the performance of iris recognition Investigate iris recognition with respect to print spoofing attacks. Plausibility of identity obfuscation and identity impersonation is established Contact Lens Detection dataset Address unseen lens patterns present in iris recognition. Introduce Binarized Statistical Image Features to capture difference in textural information between images rcle rectification converts non-orthogonal iris to approximately orthogonal. Edge-type multi-scale maps characterize iris pattern. Matching is performed using an ensemble of weak classifiers Biorthogonal Wavelet Networks (BWN) are trained. Non-ideal factors are adjusted by repositioning BWN. Synthetic irises are generated. Tests are performed on real and synthetic Human eye model and ray- tracing techniques are used to compute transformation function that reconstructs the off-angle iris to its frontal, non-refracted state Predict
  • 25. 25 Unconstrained Propose quality measure that handles segmentation errors and alignment variations. Treats different regions separately and combines results depending on the quality of the region Algorithm is proposed for segmentation that can handle variable resolutions, illumination, and occlusion
  • 26. 26 Template Aging Investigate change in irides with time. Feature extraction is performed using 1D log-Gabor filter. Obtain information by decomposition of iris into complex-valued phase coefficients Iris template aging study over a time-lapse of 2 years. Investigates degree to which template aging effect is related to pupil dilation and contact lenses Investigate factors which contribute towards template aging. Report change in pupil dilation, changes to enrollment and matching as significant factors ND Study whether fall in iris recognition performance occurs due to age or covariates such as poor acquisition, presence of occlusion, noise, and blur Study aging effects developed in digital image sensors over time. Propose method to investigate sensor aging by simulative ageing of iris images
  • 27. 27 Cross-Spectral Non-linear adaptive model is proposed to use visible range iris to predict value of corresponding NIR iris. Predicted value is compared with real NIR iris using log-Gabor filter Most suitable wavelength for iris recognition is found based on amount of available texture information and matching performance
  • 28. 28 Cross-Sensor Use three commercial iris sensors and three iris matching systems to investigate impact of cross-sensor matching on system performance in comparison to single-sensor performance Features are extracted and classification model for iris sensor is applied. Used to classify probe into a camera class. Selective enhancement algorithm is applied for respective sensor Objective function minimizes misclassification error and achieves sparsity in coupled feature spaces. Employs regularization model based on half-quadratic optimization Propose framework to learn transformations on iris biometrics. Framework is applied to reduce distance between intra-class samples and increase distance between inter-class samples
  • 29. PERIOCULAR: Periocular • Verification and identification using periocular region • Soft biometrics • Human Performance Evaluation 29
  • 30. CURRENT RESEARCH DIRECTIONS IN PERIOCULAR BIOMETRICS: Current research directions in periocular biometrics: • Cross –Spectral periocular recognition • Anti-Spoofing measures • Unconstrained recognition at a distance 30
  • 33. 33 -Proposed periocular biometric trait. -HOG, LBP, SIFT features are extracted. LBP and SIFT are fused to achieve optimal recognition - Effect of pose variation, occlusion, template aging is studied. Score-level fusion of left and right periocular region using SIFT, HOG, LBP is applied circular LBP features for periocular recognition in visible spectrum are proposed. Normalized score-level fusion applied. Weighted sum-rule score fusion applied to left and right periocular regions Binary Pattern is fused with Kernel Correlation Feature Analysis. Recognition using fusion of Discrete Wavelet Transform and LBP MBGC LBP captures texture and color histograms are extracted followed by scorelevel fusion. Information from left and right periocular regions is fused at score-level PERICULAR RECOGNITION I
  • 34. 34 FRGC Study effect of illumination & resolution change, blurring. Highest performance degradation observed for blurring. Study conducted across sessions Demonstrate performance of LBP. Suggest that skin texture, eye folds, periocular contours adequate for verification Present genetic based Type II feature extraction for LBP. Feature optimization achieved and significant improvement in recognition accuracy reported FRGC, MBGC Score-level fusion of periocular skin texture (LBP) and color (histograms) information. Establishes that periocular appearance is unique for each eye Studies performance variation for scale, pose, occlusion, pigmentation variation. Non- linear score-level fusion applied to LBP, SIFT, HOG descriptors PERICULAR RECOGNITION II
  • 35. 35 Present periocular acquisition system using PTZ camera. Walsh-Hadamard transform encoded local binary pattern applied to extract information and Non-ideal recognition: illumination variation using homomorphic filters and Self-Quotient images; pose change through geometric transformations Study effectiveness of Globally Coherent Elastic Graph Matching to compensate distortions due to expressions Apply subspace representations on Discrete Transform encoded LBP. Periocular performance is at par with face recognition. Gains tolerance to occlusion Neural networks train classifiers on cross-spectral images using Pyramid of HOG. Score-level sum rule fusion applied to NIR left and right eye images PERICULAR RECOGNITION III
  • 36. FUSING OCULAR INFORMATION: Fusing ocular information Intra – ocular Fusion Fusion of ocular traits with other biometric modalities 36
  • 39. FUSION OF OCULAR TRAITS WITH OTHER BIOMETRIC MODALITIES: 39
  • 40. FUSION OF OCULAR TRAITS WITH OTHER BIOMETRIC MODALITIES(COUNT1): 40
  • 41. DATASETS AND SOFTWARE: Datasets and Software: Datasets CASIA Iris Datasets Notre Dame and NIST Datasets UBIRIS and NICE Datasets The VARIA Retina Database Face Datasets used for Ocular Recognition Multimodal databases that include ocular modalities Open source and commercial soft wares Libor Madek’s MATLAB Source Code for Biometric Identification system based on Iris Pattern The Video Based Automatic Systems For Iris Recognition VeriEye The Eye movement classification soft ware software EFDGVSv 41
  • 44. CONCLUSION: (PATH FORWARD) Improved sensing technology Exploration of advanced machine learning algorithms for better representation and classification algorithms Heterogeneous recognition Ocular recognition at a distance Multimodal ocular biometrics Benchmarking standards and open – source software 44
  • 45. 45 CONCLUSION: (REVIEW) In this presentation, these topics have been the review: ocular modalities such as iris and periocular is presented. Information fusion approaches that combine ocular modalities with other modalities are reviewed. Future research directions are presented on sensing technologies, algorithms, and fusion approaches.
  • 46. THE END Any Questions? Thanks for your attention 46