Robust Feature Extraction and Iris Recognition for Biometric Personal Identification

Humans have distinctive and unique traits which can be used to distinguish them from other humans, acting as a form of identification. A number of traits characterising physiological or behavioral characteristics of human can be used for biometric identification. Basic physiological characteristics are face, facial thermograms, fingerprint, iris, retina, hand geometry, odour/scent. Voice, signature, typing rhythm, gait are related to behavioral characteristics. The critical attributes of these characteristics for reliably recognition are the variations of selected characteristic across the human population, uniqueness of these characteristics for each individual, their immutability over time (Jain et al.,1998). Human iris is the best characteristic when we consider these attributes. The texture of iris is complex, unique, and very stable throughout life. Iris patterns have a high degree of randomness in their structure. This is what makes them unique. The iris is a protected internal organ and it can be used as an identity document or a password offering a very high degree of identity assurance. Also the human iris is immutable over time. From one year of age until death, the patterns of the iris are relatively constant (Jain et al.,1998, Adler,1965). Because of uniqueness and immutability, iris recognition is one of accurate and reliable human identification technique. Nowadays biometrics technology plays important role in public security and information security domains. Iris recognition is one of the most reliable and accurate biometrics that plays an important role in identification of individuals. The iris recognition method deliver accurate results under varied environmental circumstances. Iris is the part between the pupil and the white sclera. The iris texture provides many minute characteristics such as freckles, coronas, stripes, furrows, crypts (Adler,1965). These visible characteristics are unique for each subject. Iris recognition process can be separated into these basic stages: iris capturing, preprocessing and recognition of the iris region. Each of these steps uses different algorithms. Pre-processing includes iris localization, normalization, and enhancement. In iris localization step, the detection of the inner (pupillary) and outer (limbic) circles of the iris and the detection of the upper and lower bound of the eyelids are performed. The inner circle is located on the iris and pupil boundary, the outer circle is located on the sclera and iris boundary. Today researchers follow different methods in finding pupillary and limbic

[1]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  T. Tan,et al.  Iris Recognition Based on Multichannel Gabor Filtering , 2002 .

[3]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[4]  Rishi Gupta,et al.  Iris Recognition System , 2010 .

[5]  Libor Masek,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2003 .

[6]  Tieniu Tan,et al.  A fast and robust iris localization method based on texture segmentation , 2004, SPIE Defense + Commercial Sensing.

[7]  Rahib Abiyev,et al.  An efficient fractal measure for image texture recognition , 2009, 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control.

[8]  F. Scotti.  Computational intelligence techniques for reflections identification in iris biometric images , 2007, 2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[9]  Koray Altunkaya,et al.  Neural network based biometric personal identification with fast iris segmentation , 2009 .

[10]  Jaihie Kim,et al.  A Novel Method to Extract Features for Iris Recognition System , 2003, AVBPA.

[11]  Mark J. T. Smith,et al.  Iris-Based Personal Authentication Using a Normalized Directional Energy Feature , 2003, AVBPA.

[12]  Jaihie Kim,et al.  Iris Feature Extraction Using Independent Component Analysis , 2003, AVBPA.

[13]  J. Daugman,et al.  Recognizing iris texture by phase demodulation , 1994 .

[14]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.

[15]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Koray Altunkaya,et al.  Personal Iris Recognition Using Neural Network , 2008 .

[17]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[18]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[19]  Dexin Zhang,et al.  Local intensity variation analysis for iris recognition , 2004, Pattern Recognit..

[20]  Sharath Pankanti,et al.  Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society , 1998 .

[21]  Yong Wang,et al.  Iris recognition using independent component analysis , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[22]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[23]  Stéphane Mallat,et al.  Zero-crossings of a wavelet transform , 1991, IEEE Trans. Inf. Theory.

[24]  Kemal Kilic,et al.  Adaptive Iris Segmentation , 2009, ISA.

[25]  Rahib Hidayat Abiyev,et al.  Iris Recognition for Biometric Personal Identification Using Neural Networks , 2007, ICANN.

[26]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[27]  Patrick J. Flynn,et al.  Experiments with an improved iris segmentation algorithm , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[28]  Carmen Sanchez-Avila,et al.  Iris-based biometric recognition using dyadic wavelet transform , 2002 .

[29]  Øivind Due Trier,et al.  Evaluation of Binarization Methods for Document Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Lu Xu,et al.  A Rapid Iris Location Method Based on the Structure of Human Eyes , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[31]  Richa Singh,et al.  Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Okhwan Byeon,et al.  Efficient Iris Recognition through Improvement of Feature Vector and Classifier , 2001 .

[33]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Lionel Torres,et al.  Person Identification Technique Using Human Iris Recognition , 2002 .

[35]  Hiroshi Nakajima,et al.  An Effective Approach for Iris Recognition Using Phase-Based Image Matching , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Zied Lachiri,et al.  Biometric personal identification system using the ECG signal , 2013, Computing in Cardiology 2013.

[37]  Jinyu Zuo,et al.  An Automatic Algorithm for Evaluating the Precision of Iris Segmentation , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[38]  John Daugman,et al.  Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition , 2003, Int. J. Wavelets Multiresolution Inf. Process..