Coarse Iris Classification by Learned Visual Dictionary

Personal identification based on biometrics technology is a trend in the future. Traditional approaches, for example, keys, ID cards, username and password, are neither satisfactory nor reliable enough in many security fields, biometrics authorizations based on face, iris, fingerprint have become a hot research filed. In those methods, iris recognition is regarded as a high accuracy verification technology, so that many countries have the same idea of adopting iris recognition to improve the safety of their key departments. The human iris can also be considered a valid biometrics for personal identification [Richard P W, 1996]. Biometrics recognition based on iris patterns is a hotspot as face recognition and fingerprint recognition recently years. The iris is the colored ring on the human eye between the pupil and the white sclera. Lots of physical biometric can be found in the colored ring of tissue that surrounds the pupil, such as corona, crypts, filaments, flecks, pits, radial furrows and striations. The iris features can be encoded by mathematical representation so that the patterns can be compared easily. In real-time iris recognition application system, iris localization is a very important step for iris recognition. The iris regions segmentation accuracy and localization real-time performance will affect the whole recognition system’s correct rate and effectiveness for large-scale database. Because iris region is a small object and has low grey value, it is very difficult to capture high contrast iris image clearly. In order to improve iris image contrast, usually some illuminations such as near infrared light source are used to increase intensity; however these illuminations may result in some faculas in iris image and affect iris segmentation and iris features. Here, we will discuss iris recognition system’s algorithm, all steps of iris recognition system will be introduced in details. Finally, we will show the experimental results based on iris database.

The ability to identify individuals based on their iris is known as iris recognition. Over the past decade iris recognition has garnered much attention because of its strong performance in comparison with other mainstream biometrics such as fingerprint and face recognition. Performance of iris recognition systems is driven by application scenario requirements. Standoff distance, subject cooperation, underlying optics, and illumination are a few examples of these requirements which dictate the nature of images an iris recognition system has to process. Traditional iris recognition systems, dubbed "stop and stare", operate under highly constrained conditions. This ensures that the captured image is of sufficient quality so that the success of subsequent processing stages, segmentation, encoding, and matching are not compromised. When acquisition constraints are relaxed, such as for surveillance or iris on the move, the fidelity of subsequent processing steps lessens.;In this dissertation we propose a multi-faceted framework for mitigating the difficulties associated with non-ideal iris. We develop and investigate a comprehensive iris image quality metric that is predictive of iris matching performance. The metric is composed of photometric measures such as defocus, motion blur, and illumination, but also contains domain specific measures such as occlusion, and gaze angle. These measures are then combined through a fusion rule based on Dempster-Shafer theory. Related to iris segmentation, which is arguably one of the most important tasks in iris recognition, we develop metrics which are used to evaluate the precision of the pupil and iris boundaries. Furthermore, we illustrate three methods which take advantage of the proposed segmentation metrics for rectifying incorrect segmentation boundaries. Finally, we look at the issue of iris image interoperability and demonstrate that techniques from the field of hardware fingerprinting can be utilized to improve iris matching performance when images captured from distinct sensors are involved.

— Iris recognition system provides an approach for individual identification and is regarded as the sophisticated biometric identification system. Therefore, the exclusive features of iris patterns should be extracted and processed. In this paper, a new feature extraction method according to ridgelet transform for identifying the iris images is provided. At first, after segmentation and normalization the collarette area of iris images has been extracted. Then we improve the quality of image by using median filter, histogram equalization, and the two-dimensional (2-D) Wiener filter as well. Finally, ridgelet transform is employed for extracting features and then, the binary bit stream vector is generated. The Hamming distance (HD) between the input bit stream vector and stored vectors is calculated for iris identification. The experimental results show efficiency of the proposed method. Keywords- Iris identification, ridgelet transform. I. I NTRODUCTION Biometrics is the technology of identification a person from a physical characteristic and it is based on behavioral characteristics of human structures. Among existing different structural features, it was shown that exclusive features like iris pattern have very suitable features [1]. Some properties of the human iris that enhance its suitability for automatic identification include: 1) its inherent isolation and protection from the external environment, being an internal organ of the eye, behind the cornea and the aqueous humor 2) the impossibility of surgically modifying it without high risk of damaging the user’s vision 3) its physiological response to light, which provides the detection of a dead or plastic iris and 4) never are there two irises alike, even for identical twin. Many researchers developed iris recognition systems. Daugman [2] proposed an algorithm which was based on iris codes. Integral differential operators are used to detect the centre and diameter of the iris. The image is converted from cartesian to polar transform and rectangular representation of the region of interest is generated. Feature extraction algorithm uses the complex valued 2-D Gabor wavelets to generate the iris codes which are then matched using HDs. Boles and Boashash [3] used zero crossing point of 1-D wavelet at different levels on same center circle of the iris and pupil centralism. Wildes [4] used Laplace pyramid with four various resolution levels to range and normalize correlation for comparison between input image and database images. Ma et al. [5] used 4 dimensional Haar wavelet analysis in four steps decomposition. Krichen et al. [6] used wavelet packets to produce an iris code at each

Iris Recognition (IR) across illumination variations is a problem of fundamental importance in computer vision. In this paper, we propose two novel techniques, viz., Triangular DCT based Feature Extraction (T-DCT) and Radon Transform based Pre-processing, to improve the performance of an IR system. T-DCT is used for efficient feature extraction and Radon Transform is used for curve detection in iris images. A Binary Particle Swarm Optimization (BPSO) based feature selection algorithm is used to search the feature space for optimal feature subset. Individual stages of the IR system are examined and an attempt is made to improve each stage. Experimental results obtained by applying the proposed algorithm on three benchmark iris databases, namely Phoenix, MMU and IITD dataset, show that the proposed system out performs other IR systems. A significant increase in the recognition rate and a substantial reduction in the number of selected features are observed.

Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system.

The use of iris as biometric trait has emerged as one of the most preferred method because of its uniqueness, lifetime stability and regular shape. Moreover it shows public acceptance and new user-friendly capture devices are developed and used in a broadened range of applications. Currently, iris recognition systems work well with frontal iris images from cooperative users. Nonideal iris images are still a challenge for iris recognition and can significantly affect the accuracy of iris recognition systems. Moreover, accurate localization of different eye’s parts from videos or still images is a crucial step in many image processing applications that range from iris recognition in Biometrics to gaze estimation for Human Computer Interaction (HCI), impaired people aid or, even, marketing analysis for products attractiveness. Notwithstanding this, actually, most of available implementations for eye’s parts segmentation are quite invasive, imposing a set of constraints both on the environment and on the user itself limiting their applicability to high security Biometrics or to cumbersome interfaces. In the first part of this Chapter, we propose a novel approach to segment the sclera, the white part of the eye. We concentrate on this area since, thanks to the dissimilarity with other eye’s parts, its identification can be performed in a robust way against light variations, reflections and glasses lens flare. An accurate sclera segmentation is a fundamental step in iris and pupil localization, even in non-frontal noisy images. Furthermore its particular geometry can be fruitfully used for accurate eyeball rotation estimation. The proposed technique is based on a statistical approach (supported by some heuristic assumptions) to extract discriminating descriptors for sclera and non-sclera pixels. A Support Vector Machine (SVM) is then used as a final supervised classifier. Once the eyeball rotation angle respect to the camera optical axis is estimated and the limbus (the boundary between the iris and the sclera) is extracted, we propose a method to correct off-angle iris image. Taking into account the eye morphology and the reflectance properties of the external transparent layers, we can evaluate the distorting effects that are present in the acquired image. The correction algorithm proposed includes a first modeling phase of the human eye and a simulation phase where the acquisition geometry is reproduced and the distortions are evaluated. Finally we obtain an image which does not contain the distorting effects due to jumps in the refractive index. We show how this correction process reduces the intra-class variations for off-angle iris images.

In this study, a hybrid security system is proposed. The proposed system is composed of two subsystems namely iris recognition system (IRS) and speaker recognition system (SRS). Pre-processing, feature extraction and feature matching are the main steps of these systems. In IRS subsystem, Gaussian filter, Canny edge detector, Hough transform, and histogram equalization is performed for pre-processing, respectively. After that, by applying 4-level Discrete Wavelet Transform (DWT) to pure iris image, the iris image is decomposed into four sub-bands (LL4, LH4, HL4 and HH4). In order to extract the feature vector from iris pattern, the LH4, HL4 and HH4 sub-bands (matrices) are merged into one matrix. Finally the matrix is transformed in vector to obtain the feature vector of iris image. For SRS subsystem, the pre-processing step includes spectral arrangement, silence part removing and band limitation operations. After pre-processing, frame blocking and windowing are applied to the long-term speech samples and then Fast Fourier Transform (FFT) is performed for the each short-term speech segments (frames). Finally, the Mel Frequency Cepstral Coefficients (MFCC) technique is performed in order to obtain feature vector of the speech. The feature matching step of both IRS and SRS is implemented with Dynamic Time Warping (DTW) which is an efficient algorithm to measure the distance between two vectors. According to the DTW results, the false acceptance rate (FAR) is zero and false rejecting rate (FRR) is about 4 % for the proposed hybrid system.

The use of iris as biometric trait has emerged as one of the most preferred method because of its uniqueness, lifetime stability and regular shape. Moreover it shows public acceptance and new user-friendly capture devices are developed and used in a broadened range of applications. Currently, iris recognition systems work well with frontal iris images from cooperative users. Nonideal iris images are still a challenge for iris recognition and can significantly affect the accuracy of iris recognition systems. In this paper, we propose a method to correct off-angle iris image. Taking into account the eye morphology and the reflectance properties of the external transparent layers, we can evaluate the distorting effect that is present in the acquired image. The correction algorithm proposed includes a first modeling phase of the human eye, a segmentation of the acquired image, and a simulation phase where the acquisition geometry is reproduced and the distortions are evaluated. Finally we obtain an image which does not contain the distorting effects due to jumps in the refractive index. We show how this correction process reduce the intra-class variations for off-angle iris images.

Standoff iris recognition systems have been developed to identify not only fully cooperative individuals but also non-cooperative moving subjects. Since iris images are captured in much less constrained conditions, they are likely to be non-ideal including being off-angle. However, traditional iris recognition systems are designed for only frontal iris images. Therefore, the existing databases save binary iris codes using only frontal images for iris matching. To use these existing iris codes, standoff systems need to be compatible with existing iris matching algorithms by generating the frontal version of iris codes even for off-angle images. Since gaze estimation is the first essential component to reconstruct the frontal view of off-angle iris images, we first need to estimate the eye gaze direction from a single off-angle iris image. This paper presents a deep learning-based gaze angle estimation method for standoff iris recognition frameworks. As the main contribution, our approach will allow us to estimate the gaze angle from an off-angle image using transfer learning and a regression model without segmentation and using additional hardware. The proposed CNN-based gaze estimation method is trained and tested with 108,600 off-angle images from 100 subjects. It shows high accuracy in gaze estimation with an average error of 4° in angle for off-angle images captured from every angle ranging -50° to +50° even if training images are not available for all angles.

Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.

During the image acquisition procedure of an automatic iris recognition system, the iris image with low quality may lead to the personal identification failure in some cases. Therefore it is very important to adopt the image quality evaluation procedure before the image processing. In this paper, we proposed a fast image quality evaluation method based on weighted information entropy combining iris image segmentation through localization. Through this method, we can fast grade the images and pick out the high quality iris images from the video sequence captured by the image acquisition device. Experimental results show that this method can quickly and effectively screen out appropriate images to meet the requirements of the iris recognition algorithm. It can also improve the speed and accuracy of the iris recognition system.

Current iris recognition systems usually regard poor quality iris images useless since defocused or partially occluded iris images may cause false acceptance. However, such a strategy may lose an opportunity to correctly report a genuine match with poor-quality samples. This paper proposes an adaptive iris matching method to improve the throughput of iris recognition systems. The core idea of the method is to dynamically adjust the decision threshold of iris matching module based on the quality measure of input iris image. So that the poor quality iris images also have a chance to match template database under the controlled false accept rate. Experiment results on the real system demonstrate the effectiveness of the proposed method and the recognition time is expected to be greatly reduced.

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut

Iris recognition system is one of the biometric systems in which the development is growing rapidly. In this paper, speeded up robust features (SURFs) are used for detecting and describing iris keypoints. For feature matching, simple fusion rules are applied at different levels. Contrast-limited adaptive histogram equalization (CLAHE) is applied on the normalized image and is compared with histogram equalization (HE) and adaptive histogram equalization (AHE). The aim is to find the best enhancement technique with SURF and to verify the necessity of iris image enhancement. The recognition accuracy in each case is calculated. Experimental results demonstrate that CLAHE is a crucial enhancement step for SURF-based iris recognition. More keypoints can be extracted with enhancement using CLAHE compared to HE and AHE. This alleviates the problem of feature loss and increases the recognition accuracy. The accuracies of recognition using left and right iris images are 99 and 99.5 %, respectively. Fusion of local distances and choosing suitable fusion rules affect the recognition accuracy, noticeably. The proposed SURF-based algorithm is compared with scale-invariant feature transform, histogram of oriented gradients, maximally stable extremal regions and DAISY. Results show that the proposed algorithm is robust to different image variations and gives the highest recognition accuracy.