IRIS Recognition Based On LBP and Combined LVQ Classifier

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

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.

Iris recognition has been tested to the most accurate biometrics using high resolution near infrared images. However, it does not work well under visible wavelength illumination. Sclera recognition, however, has been shown to achieve reasonable recognition accuracy under visible wavelengths. Combining iris and sclera recognition together can achieve better recognition accuracy. However, image quality can significantly affect the recognition accuracy. Moreover, in unconstrained situations, the acquired eye images may not be frontally facing. In this research, we proposed a feature quality-based multimodal unconstrained eye recognition method that combine the respective strengths of iris recognition and sclera recognition for human identification and can work with frontal and off-angle eye images. The research results show that the proposed method is very promising.

This study focuses on iris recognition using deep learning techniques. The EfficientDet network model was employed for both iris detection and recognition tasks. Four datasets were utilized to train and evaluate the deep learning network. The model was trained to extract iris features and classify individuals based on their unique iris patterns. The proposed method achieved a high recognition rate of over 98% across multiple dataset evaluations. For real‐time implementation on an embedded system, the trained model was quantized to an 8‐bit integer format to accommodate resource‐constrained devices. Despite this quantization, the recognition accuracy remained high, reaching 97%. By incorporating an Edge TPU accelerator alongside a Raspberry Pi system, the processing speed reached up to 10 frames per second during real‐time iris camera testing, demonstrating the feasibility of real‐time iris recognition. An intruder test was conducted to assess the system's robustness in preventing unauthorized access. The False Acceptance Rate (FAR) was measured to assess the likelihood of incorrectly accepting an unauthorized individual. Experimental results show that the FAR can be reduced to zero by applying additional temporal constraints, effectively preventing unauthorized individuals from passing the iris recognition‐based access control system.

This dissertation focuses on iris biometrics. Although the iris is the most accurate biometric, its adoption has been relatively slow. Conventional iris recognition systems utilize still eye images captured in ideal environments and require highly constrained subject presentation. A drop in recognition performance is observed when these constraints are removed as the quality of the data acquired is affected by heterogeneous factors. For iris recognition to be widely adopted, it can therefore be argued that the image capture must be facilitated and better performance should be achieved in less constrained imaging conditions. The research work presented in this dissertation demonstrates how performance in iris recognition systems is improved by adopting a video-based approach. The following components have been investigated in this study and presented in relevant publications: (1) Robust eye extraction method of eye images in face videos captured at a distance and on the move (2) Selection of optimal frames in iris videos (3) Iris segmentation in less constrained environments (4) An automated method for predicting inaccurate iris segmentation (5) Optimization of iris codes for improved recognition. The main results and novelties of this work include: Firstly, the development of a fast and accurate method for detecting eye images in face videos. Secondly, this work demonstrates that selection of optimal frames in NIR iris videos lead to better recognition performance. Thirdly, an accurate and robust iris segmentation model for eye images captured in uncontrolled conditions is proposed. Fourthly, this research presents a fully automated segmentation evaluation model for detection of in- correctly segmented iris images. Finally, a new method for optimization of several iris codes into a single highly optimized iris code is introduced. Our results and experiments suggest that incorporation of the above methods in traditional iris recognition systems will be useful for the adoption of this technology by a larger community.

Iris Recognition for Personal Identification System

Neural Network Approach to Iris Recognition in Noisy Environment

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.

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.

Iris recognition is one of the emerging areas as the demand for security in social and personal areas is increasing day by day. The most challenging step in the process of iris recognition is accurate iris localization. As it significantly affect the further processing of feature extraction and template matching stages. Traditional algorithms work efficiently and accurately in localizing iris from eye images taken in constrained environmental conditions. But their accuracy gets affected when eye images are taken in unconstrained environmental conditions. The proposed algorithm starts with determining the exact location of iris even in the presence of specular highlights and non-ideal environmental conditions. It also works well for noisy iris images, and the accuracy of segmentation stage has increased when intuitionistic fuzzy is applied on the UBIRIS v2 iris images.

Cross-spectral iris recognition represents the ability of the system to identify the iris images acquired in different electromagnetic spectrums. An iris captured in the near-infrared spectrum (NIR) is matched with an iris obtained in the visual light spectrum (VIS) to boost the recognition performance. In cross-spectral iris recognition, the illumination factor between NIR and VIS images significantly degrades the recognition performance. Therefore, the existing method only achieved recognition performance with an equal error rate (EER) larger than 5%, and it is a challenging issue for cross-spectral performance to have EER below 5%. In this paper, we improve iris recognition performance by concatenating the Gradientfaces-based normalization technique (GRF) to a standard (conventional) iris recognition method to alleviate the illumination effect. In addition, we integrate the GRF with a Gabor filter, a difference of Gaussian (DoG) filter, and texture descriptors, namely a binary statistical image feature (BSIF) and a local binary pattern (LBP). The experimental results show that the GRF can boost the cross-spectral iris recognition performance with an EER equals to 1.69%. In addition, the best cross-spectral iris recognition performance is achieved when the GRF is integrated with the Gabor filter and the BSIF.

Recognition of iris is basically a technique used by taking out the mathematical forms biometrically over any video-based images either of the eyes that consists of complex pattern which are exclusive and static in nature. Iris is a favoured biometric feature equated to supplementary biometrics due to its specificity and constancy characteristics. The first developed systems are often relied on bad edge recognition methods and filters. Various Recognition methods like Canny Edge Detection, Huff Transform, Gabor Filters, Dagman’s Operator Iris, are often utilized in the arena. There are some confines in the form of complex computational tactics, lack of precision for the images that contains complex noise, impediment due to lens, lashes of eyes and reflection observed in pre-work. The structure proposes a recognition of an iris or a validation system utilizing the method of Sobel Age Detection for the extraction of Iris feature. The tactic also evidences that the figurative depiction efficiently handles noise and declines, considering low resolutions, specular reflections and features that creates obstacle in the eye. The proposed system delivers improved safety results with the exact feature extraction here.

The Iris recognition is commonly used as a security system due to its robustness against imposters. Iris datasets are huge and hence those datasets occupy more space. Iris image compression has become an important part of better performance like speed and data storage. The portable Iris system is in huge demand. That portable systems need to transmit the iris images through a very small bandwidth channel. To reduce the time for transferring a huge number of data over small bandwidth channel, iris file can be compressed to some extent to minimize the size. Another problem is that when an image is captured, it captures some noise that disturbs the recognition performance, so, denoising is required for noise-free images. This paper separately analyzes the impact of wavelet compression along with denoising on iris images. The compression analysis is done using Embedded Zero Tree Wavelet, the other technique used is Set Partitioning in Hierarchical Tree and the third technique used is Spatial-Orientation Tree Wavelet. Denoising is done using different wavelets Daubechies, Haar, Biorthogonal and Fejer-Korovkin. The impact of the wavelet compression and denoising techniques on recognition performance are compared with False Rejection Rate and False Acceptance Rate. The quality of a compressed image is calculated with different quality metrics. This work establishes that compression and denoising of the images minimally affect the recognition performance.

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

In this work, we present an eye-image acquisition device that can be used as an image acquisition front-end application in compact, low-cost, and easy-to-integrate products for smart-city access control applications, based on iris recognition. We present the advantages and disadvantages of iris recognition compared to fingerprint- or face recognition. We also present the main drawbacks of the existing commercial solutions and propose a concept device design for door-mounted access control systems based on iris recognition technology. Our eye-image acquisition device was built around a low-cost camera module. An integrated infrared distance measurement was used for active image focusing. FPGA image processing was used for raw-RGB to grayscale demosaicing and passive image focusing. The integrated visible light illumination meets the IEC62471 photobiological safety standard. According to our results, we present the operation of the distance-measurement subsystem, the operation of the image-focusing subsystem, examples of acquired images of an artificial toy eye under different illumination conditions, and the calculation of illumination exposure hazards. We managed to acquire a sharp image of an artificial toy eye sized 22 mm in diameter from an approximate distance of 10 cm, with 400 pixels over the iris diameter, an average acquisition time of 1 s, and illumination below hazardous exposure levels.