Neural Network Approach to Iris Recognition in Noisy Environment

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

Iris recognition is a means of biometric identification. A key part of the recognition system using iris is the extraction of prominent texture information or features in the iris. The identification delay in iris recognition can be reduced by reducing the feature vector generated from the feature extraction of iris images. A new form of local binary pattern (LBP) termed LBPX is proposed in this paper as an iris feature extraction method. For this, input eye images are processed and converted to normalized iris images employing circular Hough transformation and Daugman’s rubber sheet model. Next, LBPX is applied to the normalized images. In this LBPX stage, rotation-invariant LBP operation takes place. The performance of LBPX based recognition system adopting iris image is evaluated in terms of accuracy and feature vector length. This is done for three datasets CASIA-IRIS-V4, UBIRIS and IITD. Results indicate that LBPX can achieve acceptable accuracy values of 97.20%, 96% and 96.40% for CASIA-IRIS-V4, UBIRIS and IITD datasets, respectively. Furthermore, results show LBPX outperforms existing feature extraction methods in terms of reduced feature-length, ensuring faster iris recognition.

The human iris is traditionally imaged in Near Infrared (NIR) wavelengths (700nm-900nm) for iris recognition. The absorption co-efficient of color inducing pigment in iris, called Melanin, decreases after 700nm thus minimizing its effect when iris is imaged at wavelengths greater than 700nm. This thesis provides an overview and explores the efficacy of iris recognition at different wavelength bands ranging from visible spectrum (450nm-700nm) to NIR (700nm-900nm) and Short Wave Infrared (900nm-1600nm). Different matching methods are investigated at different wavelength bands to facilitate cross-spectral iris recognition.;The iris recognition analysis in visible wavelengths provides a baseline performance when iris is captured using common digital cameras. A novel blob-based matching algorithm is proposed to match RGB (visible spectrum) iris images. This technique generates a match score based on the similarity between blob like structures in the iris images. The matching performance of the blob based matching method is compared against that of classical 'Iris Code' matching method, SIFT-based matching method and simple correlation matching, and results indicate that the blob-based matching method performs reasonably well. Additional experiments on the datasets show that the iris images can be matched with higher confidence for light colored irides than dark colored irides in the visible spectrum.;As part of the analysis in the NIR spectrum, iris images captured in visible spectrum are matched against those captured in the NIR spectrum. Experimental results on the WVU multispectral dataset show promise in achieving a good recognition performance when the images are captured using the same sensor under the same illumination conditions and at the same resolution. A new proprietary 'FaceIris' dataset is used to investigate the ability to match iris images from a high resolution face image in visible spectrum against an iris image acquired in NIR spectrum. Matching in 'FaceIris' dataset presents a scenario where the two images to be matched are obtained by different sensors at different wavelengths, at different ambient illumination and at different resolution. Cross-spectral matching on the 'FaceIris' dataset presented a challenge to achieve good performance. Also, the effect of the choice of the radial and angular parameters of the normalized iris image on matching performance is presented. The experiments on WVU multispectral dataset resulted in good separation between genuine and impostor score distributions for cross-spectral matching which indicates that iris images in obtained in visible spectrum can be successfully matched against NIR iris images using 'IrisCode' method.;Iris is also analyzed in the Short

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.

Nowadays, researcher is focus in developing reliable iris recognition systems for non-cooperative situations. The demand for iris recognition is increasing due to its reliability, accuracy and uniqueness. There are major factors involved in unconstrained environment such as obstruction by eyelids, eyelashes, glass frames, hair, off-angle, presence of contact lenses, poor illumination, motion blur, lighting and specular reflections, partially eye image, etc. The performance of the iris will be deteriorated and this results in lower recognition rate. In this paper, an overview of iris recognition for noisy imaging environments is presented included various related databases for iris recognition systems.

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.

The increasing reliance on Automated Teller Machines (ATMs) has highlighted the urgent need for advanced authentication mechanisms to safeguard user transactions against fraud and unauthorized access. However, traditional methods such as Personal Identification Numbers (PINs) and passwords remain vulnerable to attacks, while unimodal biometric systems face challenges of inter-class variance, environmental interference, and non-universality. Specifically, single-modality approaches and conventional ATM cameras fall short in capturing reliable biometric features under varying conditions, while the effectiveness of bimodal approaches in such environments has not been adequately investigated. Therefore, this study developed a bimodal biometric authentication system integrating face and iris recognition with Gray Level Co-Occurrence Matrix (GLCM) for enhanced ATM security. The system leverages GLCM for powerful texture feature extraction from both modalities, capturing intricate spatial relationships that are difficult to spoof. The extracted feature vectors were used to train Support Vector Machines (SVM) with a Radial Basis Function (RBF) kernel as classifiers for both face and iris recognition. The final authentication decision was made using Boolean OR rule fusion. The system achieved a remarkable accuracy of 98.2%, with a False Acceptance Rate (FAR) of 1.8% and a False Rejection Rate (FRR) of 1.2%. These results demonstrably outperformed comparable unimodal systems and existing biometric ATMs, validating the proposed framework as a highly secure and efficient solution for financial authentication systems.

In normal human communication, people face the speaker when listening and usually pay attention to the speaker' face. Therefore, in robot audition, the recognition of the front talker is critical for smooth interactions. This paper presents an enhanced speech detection method for a humanoid robot that can separate and recognize speech signals originating from the front even in noisy home environments. The robot audition system consists of a new type of voice activity detection (VAD) based on the complex spectrum circle centroid (CSCC) method and a maximum signal-to-noise (Max-SNR) beamformer. This VAD based on CSCC can classify speech signals that are retrieved at the frontal region of two microphones embedded on the robot. The system works in real-time without needing training filter coefficients given in advance even in a noisy environment (SNR ≫ 0 dB). It can cope with speech noise generated from televisions and audio devices that does not originate from the center. Experiments using a humanoid robot, SIG2, with two microphones showed that our system enhanced extracted target speech signals more than 12 dB (SNR) and the success rate of automatic speech recognition for Japanese words was increased about 17 points.

In this thesis we propose a set of algorithms to reduce the false rejection rate of iris recognition. Even though high recognition accuracy is claimed for iris recognition algorithms, high false rejection rates cause the impediment in worldwide use of iris biometrics.;A novel iris segmentation algorithm for non-ideal iris images treating iris as an elliptical object is proposed. Further, quality of the extracted iris image is improved using SVM based enhancement algorithm. In this algorithm, selected enhancement algorithms globally enhance the iris image and the learning algorithm synergistically fuses local information from these intermediate enhanced images. 1D log polar Gabor wavelet is then used to extract the textural features from the enhanced iris image and Euler numbers are used to extract the topological features. The extracted textural features give a global description of the iris image whereas the topological features are rotation, translation and scaling invariant. These two features are fused using the proposed match score and decision fusion algorithms. Among the three proposed fusion algorithm, SVM learning based match score fusion algorithm outperforms other fusion algorithms. Using CASIA, Miles, UBIRIS and UPOL iris databases, experimental results show that the proposed algorithm gives reduced failure to enroll rate with comparable accuracy.

The purpose of our study was to estimate the ability of structural MRI-based texture analysis (TA) to quantify the textural changes in corpus callosum (CC) caused by Progressive Supranuclear Palsy (PSP). Three distinct TA approaches: gray level co-occurrence matrix (GLCM), local binary pattern (LBP), oriented Gaussian derivative filter-bank (O-GDFB) were exploited and compared to present a novel study that can classify PSP ( n=16) by quantifying the textural changes in CC versus that in healthy controls (HC, n=16 using T1-W MRI. Different statistical texture features were extracted using GLCM and LBP, from the five distinct CC sub-regions: CC1 (genu), CC2 (anterior-mid), CC3 (mid-body), CC4 (posterior-mid) and CC5 (splenium). Unlike statistical based TA, a modified GDFB based TA was also performed to explore the differences in filter responses in the same CC sub-regions for different orientations and scales. Finally, texture features that showed statistically highest discrimibility were input to the SVM classifier in classifying PSP and HC. With leave-one-out cross validation, SVM classification results maximum accuracy of 88%, 94% and 84% at CC3 using GLCM, LBP and O-GDFB based TA respectively. The diagnostic potential of CC TA using structural MRI can be applied on routine radiology scan without needing any additional acquisition for quantitative analysis. Significant texture alteration found at CC3 may be of great importance in the diagnosis and understanding of this pathology.

Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction, and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP is working on a grayscale level so colored iris images should be transformed into a grayscale level. The proposed system gives a high recognition rate 99.87 % on different iris datasets compared with other methods.

An improved Daugman iris recognition algorithm is provided in this paper, which embodies in two aspects: ➀ Improvement for iris localization and ➁ The improvement for both iris encoding and matching algorithms. In Step 1, the localization and shape of the pupil are roughly determined in iris image, which is used as prior knowledge to quickly locate the inner and outer boundary of iris from rough to fine scale. Eyelids, eyelashes areas and the spot in the pupil are automatically detected and removed to improve the localization accuracy. In Step 2, the possible noise from residual eyelashes is further filtered by selecting a “pure” iris area as a reference and making a validation judgment pixel-wise. Furthermore, the validation flag for each pixel is introduced into the iris encoding and matching computation, as a result, the rejection rate of iris recognition is reduced. Compared with Daugman algorithm, iris recognition test on collected human eye images shows that our proposed algorithm has an obvious improvement both on boosting the speed and reducing the rejection rate.

Iris recognition has emerged as one of the most accurate and convenient biometric for person identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris recognition accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This article presents such a periocular-assisted dynamic framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation, e.g., improvement in the recognition accuracy of 22.9%, 10.4% and 14.6% on three databases under both the verification and recognition scenarios.

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.

In order to improve the performance of iris recognition, a novel method for iris recognition based on block theory and self-adaptive feature selection is proposed in this paper. Firstly, the normalized iris image is decomposed by convolving with multi-scale and multi-orientation Gabor filters, and then separated into several blocks, the block feature vector which includes mean and variance of Gabor coefficients inside each block can be obtained through statistical techniques, the iris feature vector of the whole iris image is then constructed by conjugating the block feature vector in row column order, finally the two-classifier of iris image are established based on the most distinguishable features, and the multi-classifiers of iris image are established by voting mechanism, and the performance is test by CASIA iris database. The results show that, compared with the traditional iris recognition methods, the proposed method has improved the iris recognition rate.