Iris Recognition By ANKUSH KUMAR ( M.Tech(CS) ) 211CS2279 Using SURF. ANKUSH KUMARMay 15, 2015.

IrisIris Recognition Recognition

By

ANKUSH KUMAR (M.Tech(CS))

211CS2279

Using SURF.

ANKUSH KUMARApril 18, 2023

ContentsContents Why Iris Recognition scores over others? Anatomy of the Human Eye The Iris Iris Recognition : An Overview The process

◦ Segmentation

◦ Normalization

◦ Feature Encoding & Matching

Feature (Image Descriptor)◦ SURF

Matching Methods Applications References


Biometric FeaturesBiometric Features

Can be used to uniquely identify individuals◦Face◦Fingerprint◦Handprint◦Voice◦Iris


Why Iris Recognition scores over Why Iris Recognition scores over others?others?

Has highly distinguishing textureRight eye differs from left eyeTwins have different iris textureNot trivial to capture quality image+ Works well with cooperative subjects+ Used in many airports in the world


• Eye = Camera

• Cornea bends, refracts, and focuses light.

• Retina = Film for image projection (converts image into electrical signals).

• Optical nerve transmits signals to the brain.


IrisIris

Iris is the area of the eye where the pigmented or coloured circle, usually brown, blue, rings the dark

pupil of the eye.

Example of 10 Different People Iris


Proposed Iris Recognition SystemsProposed Iris Recognition Systems

John Daugman (1993)◦ First and most well-known

Wildes (1996)Boles (1998)Ma (2004)


Analysis & RecognitionAnalysis & Recognition

ImageCapture

Iris Segmentation

FeatureExtraction

Matching


Typical iris system configurationTypical iris system configuration

Pre processing

Feature-extraction

Identification Verification

Stored templates

Uniform distribution

Reject

Accept

Iris scan 2d image capture

Iris localization

Transform representation

comparison

enrolment

Authentication


Iris Recognition systemsIris Recognition systems The iris-scan process begins with a photograph. A specialized

camera, typically very close to the subject, not more than three feet, uses an infrared image to illuminate the eye and capture a very high-resolution photograph.This process takes 1 to 2 seconds.


PreprocessingPreprocessing

Image acquisition- Focus on high resolution and quality- Moderate illumination- Elimination of artifacts

Image localizationAdjustments for imaging contrast,

illumination and camera gain


LocalizationAcquisition

IrisCode Gabor Filters Polar Representation

Image

Demarcated Zones


Iris SegmentationIris SegmentationObjective : To isolate the actual iris region in a

digital eye imageCan be approximated by two circles

the iris/sclera boundary the iris/pupil boundary(interior to former)

Depends on the image qualityEx :- persons with darkly pigmented irises will

present very low contrast between the pupil and iris region if imaged under natural light


Iris LocalizationIris LocalizationNext, we must detect the outer boundaryUse canny edge detector and Hough transform


• Once the iris region is successfully segmented from an eye image, the next stage is to transform the iris region so that it has fixed dimensions in order to allow comparisons

• The normalization process will produce iris regions, which have the same constant dimensions

• Two photographs of the same iris under different conditions will have characteristic features at the same spatial location

Normalization


The normalization process for two images of the same iris taken under varying conditions


rr

0 1

θ

θ Each pixel (x,y) is mapped into polar pair (r, ).

θ

Circular band is divided into 8 subbands of equal thickness for a given angle.

Subbands are sampled uniformly in and in r.

Sampling = averaging over a patch of pixels.

θ

θ


Iris code generationIris code generation


Image Descriptor(feature)Image Descriptor(feature) SIFT(Scale Invariant Feature Transform)

◦ GLOH (Gradient Location and Orientation Histogram)

◦ HOG (Histogram of oriented gradients)LESH (Local Energy based Shape Histogram)SURF (Speeded Up Robust Feature)

◦ Interest point detection◦ Descriptor


DetectionDetection

• Hessian-based interest point localization

• Lxx(x,σ) is the Laplacian of Gaussian of the image• It is the convolution of the Gaussian second order derivative with the

image • Lindeberg showed Gaussian function is optimal for scale-space

analysis


Detection cont…Detection cont…

Approximated second order derivatives with box filters (mean/average filter)


Detection cont…Detection cont…

Scale analysis with constant image size

9 x 9, 15 x 15, 21 x 21, 27 x 27 39 x 39, 51 x 51 …1st octave 2nd octave


DescriptionDescription

Orientation AssignmentCircular neighborhood of radius 6s around the interest point(s = the scale at which the point was detected)


DescriptionDescription

DESCRIPTOR COMPONENT


MatchingMatching Fast indexing through the sign of the Laplacian for the

underlying interest point The sign of trace of the Hessian matrix

– Trace = Lxx + Lyy

Either 0 or 1 (Hard thresholding, may have boundary effect …)

In the matching stage, compare features if they have the same type of contrast (sign)


AnalysisAnalysis

SURF is good at◦ handling serious blurring ◦ handling image rotation

SURF is poor at◦ handling viewpoint change◦ handling illumination change

SURF describes image faster than SIFT by 3 timesSURF is not as well as SIFT on invariance to

illumination change and viewpoint change


Matching MethodsMatching Methods

The Hamming distance gives a measure of how many bits are the same between two bit patterns.

In comparing the bit patterns X and Y, the Hamming distance, HD, is defined as the sum of disagreeing bits (sum of the exclusive-OR between X and Y) over N, the total number of bits in the bit pattern.

Hamming Distance


Matching MethodsMatching Methods

Normalized Correlation


An illustration of the feature encoding process. ANKUSH KUMAR

An illustration of the shifting process.The lowest Hamming distance, in this case zero, is then used

since this corresponds to the best match between the two templates.


Successful imagesSuccessful images ANKUSH KUMARApril 18, 2023

• Two IrisCodes from the same eye form genuine pair => genuine Hamming distance.

• Two IrisCodes from two different eyes form imposter pair => imposter Hamming distance.

• Bits in IrisCodes are correlated (both for genuine pair and for imposter pair).

• The correlation between IrisCodes from the same eye is stronger. ANKUSH KUMARApril 18, 2023

ProsPros Iris is currently claimed and perhaps widely believed to be

the most accurate biometric, especially when it comes to FA rates. Iris has very few False Accepts (the important security aspect).

It maintains stability of characteristic over a lifetime.

Iris has received little negative press and may therefore be more readily accepted. The fact that there is no criminal association helps.

The dominant commercial vendors claim that iris does not involve high training costs.


ConsCons There are few legacy databases. Though iris may be a good

biometric for identification, large-scale deployment is impeded by lack of installed base.

Since the iris is small, sampling the iris pattern requires much user cooperation or complex, expensive input devices.

The performance of iris authentication may be impaired by glasses, sunglasses, and contact lenses; subjects may have to remove them.

The iris biometric, in general, is not left as evidence on the scene of crime; no trace left.


ConclusionConclusion

The iris is an ideal biometric feature for human identification

Although relatively young, the field of iris recognition has seen some great successes

Commercial implementations could become much more common in the future


References “How iris recognition works” by J. Daugman,

Proceedings of 2002 International Conference on Image Processing, Vol. 1, 2002.

“Recognition of Human Iris Patterns for Biometric Identification” by Libor Masek, The University of Western Australia, 2003

http://en.wikipedia.org/wiki/SURF Patch Descriptors, by :- Larry Zitnick (

[email protected]) “SURF: Speeded Up Robust Features”. IEEE Explore By

Herbert Bay.


THANKS


Iris Recognition By ANKUSH KUMAR ( M.Tech(CS) ) 211CS2279 Using SURF. ANKUSH KUMARMay 15, 2015.

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Transcript of Iris Recognition By ANKUSH KUMAR ( M.Tech(CS) ) 211CS2279 Using SURF. ANKUSH KUMARMay 15, 2015.