ICRTEDC-2014 32

Vol. 1, Spl. Issue 2 (May, 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426GV/ICRTEDC/08

AN IMPROVED IRIS RECOGNITIONSYSTEM BASED ON 2-D DCT AND

HAMMING DISTANCE TECHNIQUESakshi Sharma

Electronics & Communication Engineering Department, Chandigarh Engineering College, Mohali, Punjabcecm.ece.ssh@gmail.com

Abstract—The biometric person authentication techniquebased onthe pattern of the human iris is well suited to beapplied to any access control system requiring a high levelofsecurity. This paper proposes a new iris recognitionsystem that implements Integro-Differential, DaugmanRubber Sheet Model, 2-D DCT, Hamming Distance toexact features from the iris and matching it with the sotreddatabase.All these image-processing algorithms have beenvalidated on noised real iris images & UBIRISdatabase.The proposed innovative technique is computationallyeffective as well as reliable in terms of recognition rates.

1. INTRODUCTIONBiometrics refers to the quantifiable data (or metrics)related to human characteristics and traits. Biometricsidentification (or biometric authentication) is used incomputer science as a form of identification and accesscontrol. It is also used to identify individuals in groups thatare under surveillance.

Biometric identifiers are the distinctive, measurablecharacteristics used to label and describe individuals.Biometric identifiers are often categorized asphysiological versus behavioral characteristics.Physiological characteristics are related to the shape of thebody. Examples include, but are not limited to fingerprint,face recognition, DNA, palm print, hand geometry, irisrecognition, retina and odour/scent. Behavioralcharacteristics are related to the pattern of behavior of aperson, including but not limited to typing rhythm, gait,and voice. Some researchers have coined the termbehaviometrics to describe the latter class of biometrics.

Types of BiometricsBiometric system is broadly categorized in two types:

Physiological and behavioral.

Figure 1: Types of biometrics

I. Working principle of biometricsBiometrics device consists of a scanning device andsoftware, that converts the gathered information intodigital form, and a database or memory that stores thebiometric data for comparison with previous records savedin the system. After converting the biometric input intodigital form, the software identifies the match points in thedata values. The match points are processed usingalgorithm into a value that can be compared withbiometric data already stored in the data base. Allbiometric systems require comparing a registeredbiometric sample against a newly captured biometricsample.

Advantages of Using Biometrics:Easier fraud detection.Better than password/PIN or smart cards.No need to memorize passwords.Require physical presence of the person to be identified.Physical characteristics are unique.It providesaccurate results.

2. BACKGROUNDThe below table shows the related research work:

RESEARCHER NAME

YEAR

ALGORITHM USED

DRAWBACKS

John G.Daugman

1994 Integro-Differential,DaugmanRubber SheetModel, 2-DGabor Filter,XORoperatorHammingDistance.

Integro-differentialoperator failsin case ofnoise and totalexecution timeis also veryhigh.

W. W. Bolesand B.Boashash

1998 1-D wavelettransforms,Edgedetectiontechnique,Zero crossingrepresentation.

Algorithms aretested on fewnumber of Irisimages,Correctrecognitionrate is 92%,Equal Errorrate is 8.13%.

33 ICRTEDC -2014

Zhonghua Linand Bibo Lu

2010 MorletwavelettransformsPolar co-ordinatetransform.

Recognitionrate is low ofthe system.

Bimi Jain, Dr.M.K. Guptaand Prof. JyotiBharti

2012 Fast Fouriertransform,Euclideandistance formatching.

Algorithmtested only on10 images,FAR and FRRare also notdeclared andEuclideandistancetechniquemakecomputationalslow.

Mohd. T.Khan

2013 1-D LogGabor filter,K-dimensionaltree techniquefor matching.

Searchefficiency isdecreased bylarge tree sizeand FAR,FRR, ERR arenot mentionedin results.

3. PROPOSED APPROACH

Figure 2: Proposed approach

SEGMENTATIONThe color image is firstly converted into gray scale image;it means that the luminance of colored image is convertedinto gray shade. The first stage of iris recognition is toisolate the actual iris region in a digital eye image. The irisregion, shown in Figure 4, can be approximated by twocircles, first one is for the iris boundary region and secondone is for the pupil boundary region. The eyelids andeyelashes cover the upper and lower parts of the irisregion. Specular reflections can also occur within the irisregion resulting into corrupting the iris pattern.

Figure 3:Grayscale iris image

NORMALIZATIONOnce the segmentation module has estimated the iris’sboundary, the normalization process will transform thecircular iris region into another shape which will have thesame constant dimensions [8]. We can be usingDaugman’s Rubber Sheet Model for normalization. Thismodel transforms the iris texture from Cartesian to polarcoordinates. This process is called as iris unwrapping,which have a rectangular entity that is used for furthersubsequent processing. The transformation of normalCartesian to polar coordinates is recommended whichmaps the entire pixels in the iris area into a pair of polarcoordinates (r, θ), where r and θ represents the intervals of[0 1] and[0 2π] as shown in figure 5.

Daugman’s Rubber Sheet Model:For normalization Daugman has invented the RubberSheet Model in which he remaps each point within the irisregion to a pair of polar coordinates (r,θ) where r is on theinterval [0,1] and θ is angle [0,2π]. Normalisation accountsfor variations in pupil size due to changes in externalillumination that might influence iris size, it also ensuresthat the irises of different individuals are mapped onto acommon image domain in spite of the variations in pupilsize across subjects etc.

Figure 4: Normalized Iris

FEATURE EXTRACTIONAfter the iris is normalized, it is compressed by usingmathematical functions and converted in to binary forms.Each isolated iris pattern is then encoded using DCTmethod to extract its binary information.Discrete Cosine Transform: A discrete cosine transform(DCT) expresses a finite sequence of data points in termsof a sum of cosine functions oscillating at differentfrequencies. DCT algorithm is very efficient in imagecompression applications which makes furthercomputational easy in the system. Discrete cosinetransform provides the output in the form of matrix.

33 ICRTEDC -2014

Zhonghua Linand Bibo Lu

2010 MorletwavelettransformsPolar co-ordinatetransform.

Recognitionrate is low ofthe system.

Bimi Jain, Dr.M.K. Guptaand Prof. JyotiBharti

2012 Fast Fouriertransform,Euclideandistance formatching.

Algorithmtested only on10 images,FAR and FRRare also notdeclared andEuclideandistancetechniquemakecomputationalslow.

Mohd. T.Khan

2013 1-D LogGabor filter,K-dimensionaltree techniquefor matching.

Searchefficiency isdecreased bylarge tree sizeand FAR,FRR, ERR arenot mentionedin results.

3. PROPOSED APPROACH

Figure 2: Proposed approach

SEGMENTATIONThe color image is firstly converted into gray scale image;it means that the luminance of colored image is convertedinto gray shade. The first stage of iris recognition is toisolate the actual iris region in a digital eye image. The irisregion, shown in Figure 4, can be approximated by twocircles, first one is for the iris boundary region and secondone is for the pupil boundary region. The eyelids andeyelashes cover the upper and lower parts of the irisregion. Specular reflections can also occur within the irisregion resulting into corrupting the iris pattern.

Figure 3:Grayscale iris image

NORMALIZATIONOnce the segmentation module has estimated the iris’sboundary, the normalization process will transform thecircular iris region into another shape which will have thesame constant dimensions [8]. We can be usingDaugman’s Rubber Sheet Model for normalization. Thismodel transforms the iris texture from Cartesian to polarcoordinates. This process is called as iris unwrapping,which have a rectangular entity that is used for furthersubsequent processing. The transformation of normalCartesian to polar coordinates is recommended whichmaps the entire pixels in the iris area into a pair of polarcoordinates (r, θ), where r and θ represents the intervals of[0 1] and[0 2π] as shown in figure 5.

Daugman’s Rubber Sheet Model:For normalization Daugman has invented the RubberSheet Model in which he remaps each point within the irisregion to a pair of polar coordinates (r,θ) where r is on theinterval [0,1] and θ is angle [0,2π]. Normalisation accountsfor variations in pupil size due to changes in externalillumination that might influence iris size, it also ensuresthat the irises of different individuals are mapped onto acommon image domain in spite of the variations in pupilsize across subjects etc.

Figure 4: Normalized Iris

FEATURE EXTRACTIONAfter the iris is normalized, it is compressed by usingmathematical functions and converted in to binary forms.Each isolated iris pattern is then encoded using DCTmethod to extract its binary information.Discrete Cosine Transform: A discrete cosine transform(DCT) expresses a finite sequence of data points in termsof a sum of cosine functions oscillating at differentfrequencies. DCT algorithm is very efficient in imagecompression applications which makes furthercomputational easy in the system. Discrete cosinetransform provides the output in the form of matrix.

33 ICRTEDC -2014

Zhonghua Linand Bibo Lu

2010 MorletwavelettransformsPolar co-ordinatetransform.

Recognitionrate is low ofthe system.

Bimi Jain, Dr.M.K. Guptaand Prof. JyotiBharti

2012 Fast Fouriertransform,Euclideandistance formatching.

Algorithmtested only on10 images,FAR and FRRare also notdeclared andEuclideandistancetechniquemakecomputationalslow.

Mohd. T.Khan

2013 1-D LogGabor filter,K-dimensionaltree techniquefor matching.

Searchefficiency isdecreased bylarge tree sizeand FAR,FRR, ERR arenot mentionedin results.

3. PROPOSED APPROACH

Figure 2: Proposed approach

SEGMENTATIONThe color image is firstly converted into gray scale image;it means that the luminance of colored image is convertedinto gray shade. The first stage of iris recognition is toisolate the actual iris region in a digital eye image. The irisregion, shown in Figure 4, can be approximated by twocircles, first one is for the iris boundary region and secondone is for the pupil boundary region. The eyelids andeyelashes cover the upper and lower parts of the irisregion. Specular reflections can also occur within the irisregion resulting into corrupting the iris pattern.

Figure 3:Grayscale iris image

NORMALIZATIONOnce the segmentation module has estimated the iris’sboundary, the normalization process will transform thecircular iris region into another shape which will have thesame constant dimensions [8]. We can be usingDaugman’s Rubber Sheet Model for normalization. Thismodel transforms the iris texture from Cartesian to polarcoordinates. This process is called as iris unwrapping,which have a rectangular entity that is used for furthersubsequent processing. The transformation of normalCartesian to polar coordinates is recommended whichmaps the entire pixels in the iris area into a pair of polarcoordinates (r, θ), where r and θ represents the intervals of[0 1] and[0 2π] as shown in figure 5.

Daugman’s Rubber Sheet Model:For normalization Daugman has invented the RubberSheet Model in which he remaps each point within the irisregion to a pair of polar coordinates (r,θ) where r is on theinterval [0,1] and θ is angle [0,2π]. Normalisation accountsfor variations in pupil size due to changes in externalillumination that might influence iris size, it also ensuresthat the irises of different individuals are mapped onto acommon image domain in spite of the variations in pupilsize across subjects etc.

Figure 4: Normalized Iris

FEATURE EXTRACTIONAfter the iris is normalized, it is compressed by usingmathematical functions and converted in to binary forms.Each isolated iris pattern is then encoded using DCTmethod to extract its binary information.Discrete Cosine Transform: A discrete cosine transform(DCT) expresses a finite sequence of data points in termsof a sum of cosine functions oscillating at differentfrequencies. DCT algorithm is very efficient in imagecompression applications which makes furthercomputational easy in the system. Discrete cosinetransform provides the output in the form of matrix.

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MATCHINGThe matching algorithm consists of all the imageprocessing steps that are carried out at the time ofenrolling the encoded iris template in database. Once thebit encrypted bit pattern B’ corresponding to binary imageformed is extracted, it is tried to match with all storedencrypted bit patterns B using simple Boolean XORoperation[2]. The dissimilarity measure between any twoiris bit patterns is computed using Hamming Distance(HD) which is given as,

Where, N=total number of bits in each bit pattern. As HDis a fractional measure of dissimilarity with 0 representingA perfect match, a low normalized HD implies strongsimilarity of iris codes.

FIGURE 5: IRIS RECOGNITION TECHNOLOGY[21]

4. RESULTS & CONCLUSIONSThis work proposes a modified iris recognition systembased on 2D DCT and Daughman rubber sheet is used fornormalization is based on minimizing the effect of theeyelids and eyelashes by trimming the iris area above theupper and the area below the lower boundaries of thepupil. The Experimental results also indicate that theperformance of the proposed technique is computationallyeffective as well as reliable in terms of recognition rate of93.2%. The combination of Daughman rubber sheet and2D DCT is promising.

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Frontiers for Emerging Identification Market, Comm. ACM,pp. 91-98, Feb. 2000.

[2] A. Ross, D. Nandakumar, A.K. Jain, Handbook ofMultibiometrics, . Springer, Heidelberg (2006).

[3] J. Daugman , How iris recognition works, IEEE Trans.onCircuits and Systems for Video Technology., Vol. 14,No. 1,pp. 21-30, January 2004.

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[7] J. Daugman, C.Downing, Epigenetic randomness,complexityand singularity of human iris patterns, Proc. R.Soc. Lond. B268, pp. 1737–1740, 2001.

[8] J. Daugman , How iris recognition works, IEEE Trans.onCircuits and Systems for Video Technology., Vol. 14,No. 1,pp. 21-30, January 2004.

[9] Center for Biometrics and Security Research, CASIA IrisImageA. K Jain, P. Flynn, and A. Ross, Handbook ofBiometrics, New York: Springer, 2008.

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[16]B.Sabarigiri1, T.Karthikeyan2, “Acquisition of Iris Images,Iris Localization, Normalization and Quality Enhancementfor Personal Identification”, International Journal ofEmerging Trends &Technology in Computer Science(IJETTCS),Volume 1, Issue 2, July – August 2012.

[17] Jain, A., Hong, L., & Pankanti, S. (2000). "BiometricIdentification". Communications of the ACM,43(2), p. 91-98. DOI 10.1145/328236.328110.

[18] Mayuri Memane, Sanjay Ganorkar, “DWT Based IrisRecognition”, International Journal of Engineering Scienceand Technology (IJEST),Vol. 4 No.08 August 2012.

[19] John G. Daugman, (1994), “Biometric PersonalIdentification System based on Iris Analysis”, U.S. Patentno. 5291560 A, publish date 1 march, 1994.

[20] Boles and Boashash, (1998), “A Human IdentificationTechnique Using Images of the Iris and WaveletTransform”, IEEE Transactions on Signal Processing, vol.46, no. 4, pp. 1185-1188.

[21] www.google.com