Iris Image Recognition Research Articles

Iris image retrieval using partial matching of image blocks

AbstractThe problem of human identification through recognition of patterns in iris images captured in unconstrained environments results in image artefacts such as image occlusion and specular reflection, in which iris tissue is observable to extremely low content. To overcome this problem, this paper presents a novel method for iris image retrieval and recognition based on partial pattern matching. The main contribution of the proposed method relies on an image partitioning schema in which the iris is divided into non‐overlapping blocks with varying dimensions, facilitating the identification and removal of image regions impaired by eyelids, eyelashes, and specular reflections. In fact, the blocks that contain artefacts are completely ignored and those blocks are preserved that include useful patterns for identification. In addition, a multi‐feature similarity followed by a score fusion technique is employed for ranking the retrieval results. The remarkable results of the classification stage include an accuracy of 100%, 98.75%, and 99.94% on three benchmark databases, including UPOL, UBIRIS.V2, and CASIA‐Iris‐Interval.v3, respectively. Additionally, in the retrieval stage, the proposed method achieves a precision of 100% on all three benchmark databases, a recall of 83.33%, and a F1‐measure of 90.91 on the CASIA‐Iris‐Interval.v3 dataset.

Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis

The use of biological and behavioural characteristics has significantly increased in recent years. Like fingerprints, faces, iris, and others in numerous crucial applications in security and electronic governance. Where recently, Research has concentrated on illuminating the recognition of iris images obtained in different fields across spectra by sensors that can capture double iris images in various areas (environment/lighting) by infrared Nearby and visible illumination (NIR, VIS) due to features unique in the iris texture that differ for the same individual, between their left and right eyes. But there is challenging to match NIR and VIS photographs due to the spectrum differences between pictures captured by near-infrared and visible light. We suggest utilizing the Histogram of Oriented Gradient (HOG) approach to extract meaningful information from images of the iris and implement Normalization to reduce differences in illumination, also apply the LDA for dimensionality reduction and K-Nearest Neighbor and Support Vector Machine as a classifier. The experimental findings were implemented on the PolyU bi-spectral dataset, and the results show that the suggested approach achieved exceptional matching performance by KNN, SVM on (Right to Left) for each (NIR, VIS) or versa (99.14%,99.04%, 97.44%,96.38%) respectively.

Iris Image Preprocessing and Recognition System

Introduction: Iris recognition framework has gotten vital, particularly in the field of safety, since it gives high reliability. Iris surface is a natural secret phrase that enjoys incredible benefits like inconstancy, soundness, unique highlights for every individual, and its significance in the security field. This makes an iris acknowledgment framework upper of other biometric strategies utilized for human identification. Iris is a hued muscle present inside the eye which helps in controlling the measure of light entering the eye. It has a few extraordinary textural data, which doesn’t get modified or altered effectively, making it a most appropriate quality for biometric frameworks. Because of its uniqueness, all-inclusiveness, unwavering quality, and strength, Iris designs serve a significant job in potential acknowledgment or verification applications. Numerous analysts have proposed new techniques to the iris acknowledgment framework to expand the productivity of the framework. Methods: A total of 460 images were taken into account. First and foremost picture pre-processing is done on the information picture to eliminate undesirable clamor from it and then different segmentation strategies, for example, edge recognition, Camus and Wildes, and so forth are applied for the proficient identification of the inner and outer boundary of the iris. Results: Iris Segmentation was done for MMU Database which contains 460 iris images. Results are recorded. Results from these indicate that it has over Seventy six percentage. Furthermore, same Wildes methods can be done with different databases like CASIA, IITD, UPOL. Results may vary from MMU database. Conclusion: Our study showed more than seventy six percentage of success rate for this particular database. Live iris templates also can be obtained and segmentation techniques can be used and find the accuracy.

Hybrid deep convolutional neural models for iris image recognition

Hybrid deep convolutional neural models for iris image recognition

Investigation on automated surveillance monitoring for human identification and recognition using face and iris biometric

Nowadays, Biometric system automatically identifies the unique feature of an individual for better evaluation and verification in recognition systems. Face and iris recognition in biometric identification systems is considered as most accurate procedure with higher recognition rate. CCTV surveillance plays a major role in human recognition and identification with the help of intelligent systems. The biometric system combined with CCTV output analyzes the data with/without human intervention. This paper presents an approach of human identification with recognition using facial and iris biometric from lower resolution images. Also, Lower resolution in image clarity is a major constraint in recognizing the individuals from distance with biometric values. To overcome problem in individual recognizing, the combination of Gabor and Legendre filter is combined. The use of hybrid log-Gabor–Legendre (LGL) filter improves the recognition pattern of face and iris in multi-spectral images. After log Gabor–Legendre filtration, two new techniques such as phase quadrant method for iris and LGBPHS method for face are used to improve recognition pattern. Hence, a framework comprising of feature recognition using LGL filter and similarity comparison using score level fusion is proposed. A series of stages enhances well the recognition performances using the proposed solution. Experiments established the validity against existing linear techniques for facial and iris image recognition pattern from CCTV cameras for automated human identification and verification.

Computer vision algorithms for dominant contact lens feature extraction using fuzzy-logic-based classifications

Iris image recognition is an emerging approach for human identification but offers low reliability. An algorithm for dominant contact lens feature extraction based on an improved neighboring binary pattern (NBP) approach is proposed herein. Features are compared with neighboring features in various directions, assigning a value of 1 to dominant features and 0 otherwise. The features in two-dimensional binary tables are then trained using an adaptive neuro fuzzy inference system (ANFIS) and classified using various classifiers. The performance of various feature descriptors based on the classification algorithms is measured and compared using parameters such as the accuracy, training time, positive acceptance rate (PAR), and negative acceptance rate (NAR), and the PAR and NAR are compared based upon a confusion matrix of classifiers. The proposed dominant feature extraction method achieves an accuracy rate of 95.7%.

Novel Optimization Based Hybrid Self-Organizing Map Classifiers for Iris Image Recognition

The concern over security in all fields has intensified over the years. The prefatory phase of providing security begins with authentication to provide access. In many scenarios, this authentication is provided by biometric systems. Moreover, the threat of pandemic has made the people to think of hygienic systems which are noninvasive. Iris image recognition is one such noninvasive biometric system that can provide automated authentication. Self-organizing map is an artificial neural network which helps in iris image recognition. This network has the ability to learn the input features and perform classification. However, from the literature it is observed that the performance of this classifier has scope for refinement to yield better classification. In this paper, heterogeneous methods are adapted to improve the performance of the classifier for iris image recognition. The heterogeneous methods involve the application of Gravity Search Optimization, Teacher Learning Based Optimization, Whale Optimization and Gray Wolf Optimization in the training process of the self-organizing map classifier. This method was tested on iris images from IIT-Delhi database. The results of the experiment show that the proposed method performs better.

Multi-Biometric Watermarking Scheme Based on Interactive Segmentation Process

Iris-based security systems are highly recommended because of their security and ease of use. Different watermarking techniques have been presented to provide security while exchanging or storing iris images. The previous iris image watermarking techniques have successfully reached their aims, however, they suffer from some limitations such as the distortions that are presented in the iris region because of the watermark embedding process, the limited embedding capacity, and the lack of robustness against unintentional attacks. On the other hand, nowadays, the biometric-based security systems have directed their interest towards multi-biometric techniques in order to improve the performance of the individual’s recognition process. This paper presents a new multi-biometric watermarking (MBW) scheme in which the features of the fingerprint image and some personal information are embedded in the iris image. To avoid the abovementioned limitations, the proposed scheme presents an interactive segmentation algorithm (ISA) and a Slantlet transform based watermark embedding method. The proposed ISA prevents any distortion in the iris region which is a beneficial feature for the iris image recognition process. The experimental results proved the efficiency of the proposed ISA in comparison with the Hough transform based methods in terms of accuracy, embedding capacity, and execution time. The proposed MBW scheme performs better in comparison with the state-of-the-art methods in terms of the intactness of the iris region, the robustness against unintentional attacks, and the watermark embedding capacity.

NIR and VW iris image recognition using ensemble of patch statistics features

A novel iris recognition system is proposed in this paper. The proposed system is able to handle various challenging issues which may occur during image acquisition in near-infrared and/or visible wavelength lights under constrained and less-constrained environments. The proposed system demonstrates great perseverance for recognizing subjects in both stable and adverse situations. During recognition, the system performs image preprocessing, feature extraction, and classification tasks. During preprocessing, an annular iris portion is segmented out from an input eyeball image, and for this, two different segmentation approaches: one for near-infrared images and another for visible wavelength images, have been proposed. A novel patch-based histogram-type feature (ensemble of patch statistics) which adopts a statistical approach of texture analysis is employed during feature extraction. For the proposed system, the extensive experimental results have been demonstrated using ten benchmark iris databases, namely MMU1, UPOL, IITD, UBIRIS.v1, CASIA-Interval-v3, CASIA-Iris-Twins, CASIA-Iris-Thousand, CASIA-Iris-Distance, CASIA-Iris-Syn, and UBIRIS.v2. The performance of the proposed system is compared with the state-of-the-art methods on these databases and the comparisons show significant out-performance on the competing methods.

Certain investigation on iris image recognition using hybrid approach of Fourier transform and Bernstein polynomials

In the era of biometric research the recent developments in real time biometric world, iris recognition is considered as one of the most important approach used in person identification based biometric authentication. This approach is considered than the other biometric authentication methods such as behavioral biometrics which specially includes Keystroke, Speaker Recognition whereas the another side of biometric authentication is Physical biometrics which specially includes Fingerprint Recognition, Voice Recognition, Finger Geometry Recognition and Facial Recognition. From these considerations, the hybrid approach of Fourier transform and Bernstein polynomial for iris recognition has been proposed. The novelty of this proposed method results in improving the Accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) for the given iris image. In addition to this, Singular Value Decomposition (SVD) is used for iris image pre-processing. Circular Hough transform (CHT) and Canny edge detection (CED) are applied for iris image segmentation which segments the individual region of the input images. After the image is segmented, Fourier transform and Bernstein polynomial have been applied to extract the features from the segmented iris image, which is the most important step for obtaining the texture details, which are independent and uncorrelated even for identical pairs. Support Vector Machine (SVM) is used for image classification. Perhaps, Feature extraction and Classification of iris image are mainly based on the rich texture details present in iris image. Finally, our proposed system is applied on UBIRIS database and our research experiment provides better accuracy and recognition rates compared to the combined iris recognition techniques such as Fourier transform with SVM, Bernstein polynomial with SVM, Fourier transform with KSVM, Bernstein polynomial with KSVM and hybridization of Fourier and Bernstein polynomial with SVM.

Super resolution and recognition of long range captured multi‐frame iris images

In this study, a framework to super resolve and recognise the long range captured iris polar images is proposed. The proposed framework consists of best frame selection algorithm, modified diamond search algorithm, Gaussian process regression (GPR) based and enhanced iterated back projection (EIBP)-based super-resolution approach, fuzzy entropy-based feature selector and neural network (NN) classifier. The framework uses linear kernel co-variance function in local patch-based GPR and EIBP algorithms and it super resolves the iris images depending on the contents of the patches, without an external dataset. NN classifier classifies the iris images by using features extracted using discrete cosine transform domain based no-reference image quality assessment model, Gray level co-occurrence matrix, Hu seven moments and statistical features. The framework is tested using CASIA long range iris database by comparing and analysing the peak signal-to-noise ratio, structural similarity index matrix and visual information fidelity in pixel domain of proposed algorithms with Yang and Nguyen framework. The results demonstrate that the proposed framework is well suited for recognition of iris images captured at a long distance.

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0–255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

Effective Approches and Challenges Focused in IRIS Image Recognition – A Survey

One of the most well-known biometric identification iris recognition system is viewed as the most accurate and reliable biometric identification system. Over the past few years, although the False Rejection Rate and Equal Error Rate of iris images are increasing and False acceptance Rate of iris images are decreasing, the iris images causes few effects due to feature extraction approach. As a result feature extraction from the actual image has to be made to provide better authentication, which is a difficult task. Various techniques have been present in the literature on iris feature extraction which has its own limitations and advantages. The purpose of this article is to present a survey of the published literature which deals with the iris feature extraction, focusing on the relative merits and demerits of a variety of Iris recognition algorithms.

Prison Identification Technology Research Based on Iris Image Recognition

Prison Identification Technology Research Based on Iris Image Recognition

Iris Image Recognition Based on Independent Component Analysis and Support Vector Machine

Iris has a very unique texture and pattern, different for each individual and the pattern will remain stable, making it possible as biometric technology called iris recognition. In this paper, 150 iris image from Dept. Computer Science, Palacky University in Olomouc iris database used for iris recognition based on independent component analysis and support vector machine. There are three steps for developing this research namely, image preprocessing, feature extraction and recognition. First step is image preprocessing in order to get the iris region from eye image. Second is feature extraction by using independent component analysis in order to get the feature from iris image. Support vector machine (SVM) is used for iris classification and recognition. In the end of this experimental, the implement method will evaluated based upon Genuine Acceptance Rate (GAR). Experimental result shown that the recognize rate from variation of training data is 52% with one data train, 73% with two data train and 90% three data train. From experimental result also shows that this technique produces good performance.

FEATURES OF THE IRIS IMAGE PRE-PROCESSING ALGORITHM

The article presents a brief overview of the not numerous issues of biometric identification based on iris. Usually, the original image corrupted by equipment scanning noise, discretization or data transmission channels, whe-reby images are uneven brightness and contrast. This leads to breaks in linear objects, masking of the analyzed complex objects, which leads to a large number of errors and failures, especially automatic identification and recog-nition systems. Preprocessing includes: equation of the general luminance background image, the elimination of the original image high-frequency noise and various arti-facts, contrast binary image and other functional trans-formations. Output description of the original informa-tion must be maximally adapted to storage, transmission and analysis of all possible solutions. But with all these transformations one of the most important indicators should be to preserve the maximum number of special «key» elements of the original information. Identified promising areas for further research in the field of iris image recognition. As the test data used the image of an open database.

Iris Recognition based on Local Mean Decomposition

The increasing need for information security has led to more attention being given to biometrics-based, automated personal identification. Among existing biometric approaches, the human iris is the mos t promising technique. In general, an iris recognition algorithm includes four basic steps: image quality assessment, image preprocessing, image feature extraction, and image matching. This paper proposes an iris image matching and recognition method based on local mean decomposition (LMD). The LMD is a multi- resolution decomposition technique employed as a low-pass filter and utilizes discriminating features for iris recognition. To evaluate the performance of this novel approach, several similarity measures were used to assess the results based on experiments using both the CASIA and ICE iris image databases. The results showed promising performance using any of the three measures. Therefore, the LMD method is a useful tool for iris feature extraction and recognition.

Functional Data Analysis as an Alternative for the Automatic Biometric Image Recognition: Iris Application

Functional data analysis has been a novel option for representing images, since the continuous nature of images is preserved. Image representation using functional data provides significant advantages, being the appreciable reduction of the dimensionality one of the most significant. This paper gives a detailed description of the entire imaging process using the proposed approach. As an example, the representation of iris images through functional data for recognition tasks was used. The paper presents experiments and results of applying this approach to the recognition of iris images, demonstrating its effectiveness.

Speech Enhancement with Local Adaptive Rank-Order Filtering

A local adaptive algorithm for speech enhancement is presented. The algorithm is based on calculation of the rank-order statistics of an input speech signal over a moving window. The algorithm varies the size and contents of a sliding window signal as well as an estimation function employed for recovering a clean speech signal from a noisy signal. The algorithm improves the quality of a speech signal preserving its intelligibility. The performance of the algorithm for suppressing additive noise in an input test speech signal is compared with that of common speech enhancement algorithms in terms of objective metrics. la misma. sed approach. As an example, the representation of iris images through functional data for recognition tasks was used. The paper presents experiments and results of applying this approach to the recognition of iris images, demonstrating its effectiveness.

Functional Data Analysis as an Alternative for the Automatic Biometric Image Recognition: Iris Application

Functional data analysis has been a novel option for representing images, since the continuous nature of images is preserved. Image representation using functional data provides significant advantages, being the appreciable reduction of the dimensionality one of the most significant. This paper gives a detailed description of the entire imaging process using the proposed approach. As an example, the representation of iris images through functional data for recognition tasks was used. The paper presents experiments and results of applying this approach to the recognition of iris images, demonstrating its effectiveness.