Iris Template Research Articles

Extracting Unique Personal Identification Number from Iris

The aim of this research is to propose an enhanced model that significantly reduces the noise in the iris templates and thus present an efficient and reliable iris recognition system. Accuracy of Iris recognition system significantly depends on reducing noise at every stage of recognition process especially at eye image acquisition and iris segmentation stages. The proposed Iris recognition model combines Canny edge detector and circular Hough transform which yields more rapid and accurate localization of Iris inner and outer boundaries. The eyelid and eyelashes occlusion is also dealt with by applying non maxima radial suppression technique. The segmented Iris area is then normalized and converted from annular shape to fixed-sized rectangular block. In this stage all iris templates converted from circular to rectangular shape will have standard dimensions thus enabling Iris recognition system to perform comparison between them. Fast Fourier Transform function is then applied to normalized Iris image. Input image is viewed as a signal that is converted from time to frequency domain in which the number of frequencies is equal to number of image pixels. The resulting matrix is consisted of real and imaginary part represented by complex numbers. The matrix is encoded according to the pre established encoding pattern that assigns certain sequence of binary numbers to each possible combination of complex numbers thus producing Iris code. In the matching phase the iris codes are compared and if the determined degree of dissimilarity between the two is below the pre-established threshold value than the codes belong to the same user. The proposed Iris recognition method, therefore, successfully treats eyelids and eyelashes noise issues and present the efficient set of algorithms whose performance is superior compared to other existing methods.

Biometric Privacy Using Secret Image Sharing

Biometric technique includes uniquely identifying person based on their physical or behavioral characteristics. It is mainly used for authentication. Iris scanning is one of the most secure techniques among all biometrics because of its uniqueness and stability (i.e., no two persons in the world can have same iris). For authentication, the feature template in the database and the user template should be the same method for extracting iris template in this proposed system. Also storing the template in the database securely is not a secure approach, because it can be stolen or tampered. To deal with this security issue, the proposed system is securely storing the template in the database by firstly using randomness to scramble the bits of template based on chaos system. Secondly, a hiding technique is utilized to hide the scrambled templates in host images randomly. Finally, a secret sharing based on linear system is implemented on the iris template in database to protect it and adding extra layer of iris authentication system. The proposed secret sharing system has been generated a meaningful shares which overcomes the problem in traditional methods. Also in proposed system, two approaches of iris extraction have been presented.

2-DWT and AES: Secure Authentication Management for Polar Iris Templates Using Visual Cryptography

Abstract Biometrics deals with authenticating a person's identity based on the physiological or behavioral characteristics. Visual cryptography (VC) is a promising information security technique that allows the secret sharing of images without any cryptographic computations. Various existing schemes were introduced for securing the raw biometric data and template in the database using the VC technique. The complexity of encryption plays a vital role in security improvement. In order to overwhelm the above limitations, a secure authentication management for polar iris templates is presented using VC technique. The collaborative splitting of pixels in all directions presented in this paper was done in order to improve security. At first, the input image was segmented using the Canny edge detection and Hough transform. Subsequently, the normalization module transformed the iris texture from the Cartesian to polar-coordinates. The polar iris image was further separated into two shares, namely, share 1 and share 2, using VC technique. To accomplish more security than the existing methods, both 2-discrete wavelet transform (DWT) and advanced encryption standard (AES) shifting techniques were introduced in VC, termed as transform based AES (TAES). After receiving the encrypted image, the feature extraction is carried out by multi-scale local binary pattern (MLBP). The share 1 images are stored in the user database, whereas the share 2 images are stored in the server database. K-NN classifier is employed to recognize and retrieve the share 2 from the user database on the basis of features. Finally, reconstruction was performed from recognized share 1 and share 2 images by using the inverse process of TAES. The experimental results exhibit better peak signal to noise ratio (PSNR), mean square error (MSE) and normalized correlation (NC), false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER) than the other existing methods.

Individuals Authentication from Both-Eye Images Using Feature Level Fusion and Score Level Fusion

In this paper, an efficient method that allows us to authenticate individuals from both-eye images using feature level fusion and score level fusion is presented. The proposed method consists of three main steps: In the first one, the iris images are segmented in order to extract the iris disc. The segmented images are normalized by Daugman rubber sheet model. In the second step, the normalized images are analyzed by a bench of two 1D Log-Gabor filters to extract the texture characteristics. The encoding is realized with a phase of quantization developed by J. Daugman to generate the binary iris templates. In the third step, feature level fusion is applied by concatenation of both binary irises templates and score level fusion is utilized using Dempster Shafer rule. For the authentication and the similarity measurement between both binary irises templates, the hamming distances are used with a previously calculated threshold. The proposal method using two fusion techniques has been tested on a subset of iris database CASIA-IrisV3-Interval. The proposal method using score level fusion based on Dempster Shafer rule shows better performance with accuracy of 99.97%, FPR of 0% FNR of 4.49%, EER of 1.4% and processing time of 15.39s for one iris image.

Cancellable iris template generation based on Indexing-First-One hashing

Eye iris has been widely recognized as one of the strongest biometrics attributed to its high accuracy performance. However, templates in conventional iris recognition systems are unprotected and highly vulnerable to numerous security and privacy attacks. Despite a number of cancellable biometric schemes have been proposed but at the expense of substantially decreased accuracy performance. In this paper, we introduce a new cancellable iris scheme, coined as “Indexing-First-One” (IFO) hashing. IFO hashing is inspired from the Min-hashing that primarily used in text retrieval domain. However, IFO hashing has been further strengthened by two novel mechanisms, namely P-order Hadamard product and modulo threshold function. The IFO hashing scheme strikes the balance between accuracy performance and privacy/security protection. Comprehensive experiments on CASIA-v3 iris benchmark database and rigorous analysis demonstrate decent accuracy performance with respect to its original counterparts yet offer strong resilience against several major security and privacy attacks.

A privacy-preserving cancelable iris template generation scheme using decimal encoding and look-up table mapping

Biometric-based recognition systems have overcome passive issues of traditional human authentication systems. However, security theft and privacy invasion are two passive issues that still persist in the effective deployment of biometric-based authentication systems. Compromise of biometric data can potentially lead to serious security violation as the user's biometric trait cannot be changed. In order to prevent the invasion of biometric templates, it is desired to morph the original biometric template through non-invertible or irreversible transformation function. This transformed template is referred to as cancelable template and can be replaced or reissued in case of compromise. In this paper, we propose a novel cancelable iris template generation technique based on randomized look-up table mapping. The technique utilizes a decimal vector generated from a rotation-invariant feature vector. The feature vector is generated using 1-D Log Gabor filter applied to the iris image. Experiments carried out on various iris databases confirm the efficacy of the proposed approach. After applying the template protection mechanism, we have achieved Equal Error Rate (EER) of 0.37%, 0.43% and 0.79% for CASIA-V 1.0, CASIA-V3-Interval and ICE 2005 iris databases, respectively. Moreover, the transformation preserves the irreversibility, revocability and diversity properties of the concealable iris templates.

Iris Recognition using Gray Level Co-occurrence Matrix and Hausdorff Dimension

Biometrics authentication is the only accurate solution for personal identification and security problems. Password incorrect use and misapplication, intentional and inadvertent is a gaping hole in security. These results are mainly occurs due to Poor human judgment, carelessness and due to tactlessness. Biometric removes all these types of security mistakes. In iris recognition system identification and verification is one of the efficient method. The objective of this proposed system is to analyze the performance of iris. The segmentation of the iris utilizes shape, intensity, and location information for pupil or iris localization and performs normalization of the iris region by unwrapping the circular region into a rectangular region. The feature extraction of iris was done by biometrics GLCM (Gray Scale Co-occurrence Matrix) and HD (Hausdorff Dimension. The BGM (Biometric Graph Matching) algorithm is used, which is used to match the graph between the training image and test image of the iris biometric. The BGM algorithm uses graph topology to define different feature values of the iris templates. A SVM (Support Vector Machine) classifier is used to distinguish between genuine and imposter. The results give better performance and authentication than the existing method.

A Framework for Iris Biometrics Protection: A Marriage Between Watermarking and Visual Cryptography

This paper presents a novel security architecture for protecting the integrity of iris images and templates using watermarking and visual cryptography (VC). The proposed scheme offers a complete protection framework for the iris biometrics which consists of two stages: the first stage is for iris image protection, while the second is for the iris template. First, for protecting the iris image, a watermark text which carries personal information is embedded in the middle band frequency region of the iris image using a novel watermarking algorithm that randomly interchanges multiple middle band pairs of the discrete cosine transform. Second, for iris template protection, the binary iris template is divided into two shares using VC, where one share is stored in the database and the other is kept with the user on a smart card. In addition, the SHA-2 hash function is utilized to maintain the integrity of the stored iris template in both the database and smart card. The experimental and comparison results on the CASIA V4 and UBIRIS V1 iris databases demonstrate that the proposed framework preserves the privacy of the iris images and templates and retains robustness to malicious attacks, while it does not have a discernible effect on the recognition performance.

Digital tags from multi-subject biometric features for joint ownership of digital data

This paper addresses the issue of joint ownership of digital images and audio signals by embedding a perceptually transparent digital signature. The pattern is generated using an approach so that the authentication of each constituent subject can be done after decomposition of the digital pattern. The features from these iris images from multiple subjects are strategically combined to generate a biometric watermark of an acceptable size under perceptual transparency constraints. This biometric watermark is embedded in SVD domain for audio signal and using a DCT-based method for digital images. The extracted watermark is decomposed into the respective iris templates for distinct identification in the database. Experimental result shows that proposed watermarking system maintains the challenging design requirements of watermarking system. Biometric watermark generated from biometric features of more than one subject is a novel effort for establishing joint ownership of multimedia data and may be considered as a significant contribution.

Critical examination of the IREX VI results

The authors analyse why Iris Exchange Report (IREX) VI conclusions about ‘iris ageing’ differ significantly from results of previous research on ‘iris template ageing’. They observe that IREX VI uses a definition of ‘iris ageing’ that is restricted to a subset of International Organization for Standardization (ISO)-definition template ageing. They also explain how IREX VI commits various methodological errors in obtaining what it calls its ‘best estimate of iris recognition ageing’. The OPS-XING dataset that IREX VI analyses for its ‘best estimate of iris recognition ageing’ contains no matches with Hamming distance >0.27. A ‘truncated regression’ technique should be used to analyse such a dataset, which IREX VI fails to do so, biasing its ‘best estimate’ to be lower-than-correct. IREX VI mixes Hamming distances from first, second and third attempts together in its regression, creating another source of bias towards a lower-than-correct value. In addition, the match scores in the OPS-XING dataset are generated from a ‘1-to-first’ matching strategy, meaning that they contain a small but unknown number of impostor matches, constituting another source of bias towards an artificially low value for ageing. Finally, IREX VI makes its ‘best estimate of iris recognition ageing’ by interpreting its regression model without taking into account the correlation among independent variables. This is another source of bias towards an artificially low value for ageing. Importantly, the IREX VI report does not acknowledge the existence of any of these sources of bias. They conclude with suggestions for a revised, improved IREX VI.

Observing the Effects of Colored Lenses onIris Recognition using Feature ExtractionTechnique

Iris templates can be matched using various matching techniques; each approach to iris recognition has several aspects being taken into consideration. One of the most important aspect being the presence of a lens in the users eye. The biometric templates, including iris and users attributes which are produced by using different recognition techniques can be matched through the Iris-templates can be replicated. From this we conclude that, without a thorough security analysis, iris templates cannot be assumed safe and secure. In this paper, we are going to discuss about the presence of a contact lens, particularly a colored lens, and the challenges we face as it alters the natural iris patterns. In this paper we will use a feature extraction method to remove the effect of colored contact lenses and make it more secure. This has proven to be very efficient and also less space consuming.

BIOMETRIC PERSONAL IDENTIFICATION ON 2D WAVELET TRANSFORM AND CHI-SQUARED MODEL

Iris recognition system consists of image acquisition, iris preprocessing, iris segmentation and feature extraction with comparism (matching) stages. The biometric based personal identification using iris requires accurate iris segmentation for successful identification or recognition. Recently, several researchers have implemented various methods for segmentation of boundaries which will require a modification of some of the existing segmentation algorithms for their proper recognition. Therefore, this research presents a 2D Wavelet Transform and Chi-squared model for iris features extraction and recognition. Circular Hough Transform was used for the segmentation of the iris image. The system localizes the circular iris and pupil region and removes the occluding eyelids and eyelashes. The extracted iris region is normalized using Daugman’s rubber sheet model into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data (iris signature) from the 2D wavelet transform data is extracted, forming the biometric template. The chi-squared distance is employed for classification of iris templates and recognition. Implementing this model can enhance identification. Based on the designed system, an FAR (False Acceptances ratio) of 0.00 and an FRR (False Rejection Ratio) of 0.896 was achieved.

Region based feature extraction from non-cooperative iris images using triplet half-band filter bank

Abstract In this paper, we have proposed energy based features using a multi-resolution analysis (MRA) on iris template. The MRA is based on our suggested triplet half-band filter bank (THFB). The THFB derivation process is discussed in detail. The iris template is divided into six equispaced sub-templates and two level decomposition has been made to each sub-template using THFB except second one. The reason for discarding the second template is due to the fact that it mostly contains the noise due to eyelids, eyelashes, and occlusion due to segmentation failure. Subsequently, energy features are derived from the decomposed coefficients of each sub-template. The proposed feature has been experimented on standard databases like CASIAv3, UBIRISv1, and IITD and mostly on iris images which encounter a segmentation failure. Comparative analysis has been done with existing features based on Gabor transform, Fourier transform, and CDF 9/7 filter bank. The proposed scheme shows superior performance with respect to FAR, GAR and AUC.

Efficient hardware implementation of AES algorithm using bio metric key

The innovations in technology add to the need for an enhanced security scheme for network transmission and data transfer through different channels. Biometrics is used in many novel ways to satisfy real time implications. Confidentiality and authentication of image data transfer over a network can be assured by combining biometrics and cryptography effectively. This paper proposes iris-based secured crypto system in which the application of AES algorithm along with the iris biometrics, used as the key in the algorithm. Images can be sent by the user by combining with the iris template features which are converted to 128 bit each suitable for the algorithm. Low power and faster computation techniques are enabled to increase the integrity of this scheme. Statistical analysis and attacks are performed on the key and images to prove their randomness, uniqueness and higher security levels.

Quality-driven and real-time iris recognition from close-up eye videos

This paper deals with the computation of robust iris templates from video sequences. The main contribution is to propose (i) optimal tracking and robust detection of the pupil, (ii) smart selection of iris images to be enrolled, and (iii) multi-thread and quality-driven decomposition of tasks to reach real-time processing. The evaluation of the system was done on the multiple biometric grand challenge dataset. Especially, we conducted a systematic study regarding the fragile bit rate and the number of merged images, using classical criteria. We reached an equal error rate value of 0.2 % that reflects high performance on this database with respect to previous studies.

Biometric Personal Identification based on Iris Patterns

This paper discusses an analysis of human iris patterns for recognition of biometric system which consists of a segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region is then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. To encode the unique pattern of the iris into a bit-wise biometric template, 1D Log-Gabor filter is used.Finally to match two iris templates hamming distance is used as matching metric. The system performance is analyzed on 312 iris images taken from standard CASIA Iris Interval database version 4. To establish the verification accuracy of iris representation and matching approach, each iris image in the database is matched with all the other iris images in the database and genuine and imposter distribution is found .The performance of the system is implemented by evaluating the Decidability Index (DI), False match rate (FMR), False Non-match rate (FNMR), Genuine Accept Rate (GAR) and Equal error rate (EER).

A Close Approach to Iris Recognition System

Iris recognition, a relatively new biometric technology, has great advantages such as variability, stability and security, thus it is the most promising for high security environments. Among its applications are border control in airports and harbors, access control in laboratories and factories, identification for Automatic Teller Machines and restricted access to police evidence rooms. There have been several implementations of security systems using biometric, especially for identification and verification cases. The term biometrics is derived from the Greek words bio (life) and metric (to measure). The pattern used in the biometric is the iris pattern in human eye. The iris pattern has been proved unique for each person. A literature review of the most prominent algorithms implemented in each stage is presented. This paper provides a review of major iris I. Introduction Biometric recognition is an emerging technology which employs the physiological and behavioral characteristics to identify an individual. The physiological characteristics include the iris, fingerprint, face and hand geometry. Voice and signature are categorized as the behavioral characteristics. Among these, the human iris is an annular region between the sclera (the white portion of the eye) and the pupil (the darkest portion of the eye). Iris is gaining lots of attention due its unique pattern. The patterns that give uniqueness to the iris are the coronas, furrows, stripes and so on (1). These patterns thus, distinguish the individual as the genuine and the imposter, and make the iris recognition particularly the promising solution. Iris recognition is a method of biometric authentication that uses pattern-recognition techniques based on high resolution images of the irises of an individual's eyes (16). The main stages of an iris recognition system are: image pre-processing which consists of iris localization, iris normalization; iris feature extraction and template matching (13)(15). It is necessary to obtain the iris region to carry out the feature extraction and the matching. Eyelids and the eyelashes that may cover the iris region are detected and removed. The normalization step is the conversion of Cartesian co-ordinates to the polar co-ordinates. Various image enhancement algorithms can also applied in order to compensate the non uniformity and low contrast characters in the iris portion. Feature extraction is the process of extracting texture from the region of interest and then using these features as parameters for comparing two iris templates. The significant features of the iris are obtained for accurate identification purpose. Template matching compares the user template with the templates from the dataset or the trained dataset using a matching metric. The matching metric will give the measure of similarity between two iris templates. This paper discuses the algorithmic implementation in each stage. Iris image capture stage will not be discussed in this paper. The rest of the stages are discussed next sections including image preprocessing, iris feature extraction and template matching.

Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

Fusing the information in visible light and near-infrared images for iris recognition

Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.

Twin Layer Iris Certification for Confidential Archive by Conceiving Shares (ICCA-CS)

Iris is one of the most challenging biometric techniques. In many proposed biometric recognition technique, template's are store in the database for comparison. But there exists a vital problem in storing the template in the database since it can be easily bootlegged. In order to overcome these issues a novel method for person identification by their Iris, through securely storing the template is proposed. Visual Cryptography Encryption (VCE) technique is used for securely storing the iris template. By means of VCE, shares are generated for the iris template which is stored in the database instead of storing the entire template. Shares are generated by pixel expansion method. Authentication is provided by comparing the shares. Comparison is done by using Hamming Code. So it is a very effective method of person identification. The proposed method gives an extra security in iris recognition.