Quality-based dynamic threshold for iris matching

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.

Iris recognition system is the most secured, and faster means of identification and authentication. However, iris recognition system suffers a setback from blurring, low contrast and illumination due to low quality image which compromises the accuracy of the system. The acceptance or rejection rates of verified user depend solely on the quality of the image. In many cases, iris recognition system with low image contrast could falsely accept or reject user. Therefore this paper adopts Histogram Equalization Technique to address the problem of False Rejection Rate (FRR) and False Acceptance Rate (FAR) by enhancing the contrast of the iris image. A histogram equalization technique enhances the image quality and neutralizes the low contrast of the image at normalization stage. The experimental result shows that Histogram Equalization Technique has reduced FRR and FAR compared to the existing techniques.

In state-of-the-art iris recognition systems, the input iris image has to be compared with a large number of templates in database. When the scale of iris database increases, they are much less efficient and accurate. In this paper, we propose a novel iris classification method to attack this problem in iris recognition systems. Firstly, we learned a small finite dictionary of visual words(clusters in the feature space), which are called Iris-Textons, to represent visual primitives of iris images. Then the Iris-Texton histograms are used to represent the global features of iris textures. Finally, K-means algorithm is used for classifying iris images into five categories. Based on the proposed method, the correct classification rate is 95% in a five-category iris database. By combining this method with traditional iris recognition algorithm, our system shows better performance in terms of both speed and accuracy.KeywordsGlobal FeatureIris ImageIris RecognitionVisual DictionaryIris DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Problem statement: In any real time biometric system processing speed and recognition time are crucial parameters. Reducing processing time involves many parameters like normalization, FAR, FRR, management of eyelid and eyelash occlusions, size of signature etc. Normalization consumes substantial amount of time of the system. This study contributes for improved iris recognition system with reduced processing time, False Acceptance Rate (FAR) and False Rejection Rate (FRR). Approach: To improve system performance and reliability of a biometric system. It avoided the iris normalization process used traditionally in iris recognition systems. The technique proposed here used different masks to filter out iris image from an eye. Comparative study of different masks was done and optimized mask is proposed. The experiment was carried on CASIA database consisting of 756 iris images of 108 persons. Each person contributes seven images of eye (108×7 = 756) images in the database. Results: In the proposed method: (1) Normalization step is avoided; (2) Computational time is reduced by 0.3342 sec; (3) Iris signature size is reduced; (4) Improved performance parameters. (With reduced feature size, proposed method achieves 99.4866% accuracy, 0.0069% FAR, 1.0198% FRR and significant increase in speed of the system). Conclusion: Iris signature proposed was comparatively small just of 1×24 size. Though Daugman’s method gives best accuracy of 99.90% but the iris signature length used by that algorithm is comparatively very high that is 1×2048 phase vector. Also Daugman has used phase information in signature formation. Our method gives a accuracy of 99.474% with a signature of comparatively very small length. This has definitely contributed to improve the speed.

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.

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 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.

The quality of iris image is a key point to affect the accuracy of iris recognition system. There are four main abnormities of captured iris image: defocus, motion blur, eyelid occlusion and eyelash occlusion. They can be distinguished by comparing different frequency components of the image. This paper describes a technique of using Field Programmable Gate Arrays (FPGA) coprocessor to assess the quality of the iris image in a Texas Instrument (TI) Digital Signal Processor (DSP) based Iris Recognition System, by implementing Laplacian Sharpening and 2D-FET transform on the chip. FPGA has become an extremely cost-effective mean of computing algorithms to improve system performance and enhance the computation capability and flexibility of the iris recognition system based on DSP.

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. However, similar to other biometric modalities, iris recognition systems are also vulnerable to presentation attacks (commonly called spoofing) that attempt to conceal or impersonate identity. Examples of typical iris spoofing attacks are printed iris images, textured contact lenses, and synthetic creation of iris images. It is critical to note that majority of the algorithms proposed in the literature are trained to handle a specific type of spoofing attack. These algorithms usually perform very well on that particular attack. However, in real-world applications, an attacker may perform different spoofing attacks. In such a case, the problem becomes more challenging due to inherent variations in different attacks. In this paper, we focus on a medley of iris spoofing attacks and present a unified framework for detecting such attacks. We propose a novel structural and textural feature based iris spoofing detection framework (DESIST). Multi-order dense Zernike moments are calculated across the iris image which encode variations in structure of the iris image. Local Binary Pattern with Variance (LBPV) is utilized for representing textural changes in a spoofed iris image. The highest classification accuracy of 82.20% is observed by the proposed framework for detecting normal and spoofed iris images on a combined iris spoofing database.

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.

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.

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. Reliability and accuracy of iris biometric modality have prompted its large-scale deployment for critical applications such as border control and national identification projects. The extensive growth of iris recognition systems has raised apprehensions about the susceptibility of these systems to various presentation attacks. In this thesis, a novel iris presentation attack using deep learning based synthetically generated iris images is presented. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, a new framework, named as iDCGAN is proposed for creating realistic appearing synthetic iris images. In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach. We also demonstrate that synthetically generated iris images can be used to attack existing iris recognition systems. As synthetically generated iris images can be effectively deployed in iris presentation attacks, it is important to develop accurate iris presentation attack detection algorithms which can distinguish such synthetic iris images from real iris images. For this purpose, a novel structural and textural feature-based iris presentation attack detection framework (DESIST) is proposed. The key emphasis of DESIST is on developing a unified framework for detecting a medley of iris presentation attacks, including synthetic iris. Experimental evaluations showcase the efficacy of the proposed DESIST framework in detecting synthetic iris presentation attacks.

— 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 feature extraction is the crucial stage of the whole iris recognition process. A brief introduction of iris recognition system is made firstly in this paper, then presented the method of iris feature extraction and matching using Scale Invariant Feature Transform (SIFT). Through analyzing iris feature extraction and matching method, we found in conventional iris recognition system, Iris features are not consistent because most feature extraction techniques are sensitive to the variations of captured iris image data, are impacted of natural illumination or other variant conditions easily. So this paper used SIFT feature which is invariant to image scaling and rotation, and partially invariant to change in illumination. They are also well localized in both the spatial and frequency domains. SIFT feature shows a higher feasibility in the iris feature extraction and matching process.

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.