A novel approach towards iris segmentation and recognition

AbstractIris segmentation is an integral part of iris recognition. Earlier works in the iris recognition uses the conventional iris localization and binarization to identify the pupil and iris boundaries. In this paper a new/ novel approach for iris segmentation is proposed where the existing canny method and local maxima suppressed image is used for identifying the boundaries and centre of pupil and iris.The paper shows the different result so obtained for iris segmentation using MATLAB software .CASIA database has been used to obtain the standardized output

A Highly Efficient Biometrics Approach for Unconstrained Iris Segmentation and Recognition

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineats the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design

Black hole algorithm along edge detector and circular hough transform based iris projection with biometric identification systems

The circular parameters between the pupil and the iris are found using current iris identification techniques but the accuracy creates an issue for the detection process during image processing. The procedure of extracting the iris region from an eye image using circular parameters can be improved via approximately too many approaches in literature but remain some portions under slightly unconstrained circumstances. In this study, we presented a Black Hole Algorithm (BHA) along the Canny edge detector and circular Hough transform-based optimization technique for circular parameter identification of iris segmentation. The iris boundary is discovered using the suggested segmentation approach and a computational model of the pixel value. The BHA looks for the central radius of the iris and pupil. The system uses MATLAB to test the CASIA-V3 database. The segmented images exhibit 98.71% accuracy. For all future access control applications, the segmentation-based BHA is effective at identifying the iris. The integration of the BHA with the Hough transforms and Canny edge detector is the main method by which the iris segmentation is accomplished. This novel technique improves the accuracy and effectiveness of iris segmentation, with potential uses in image analysis and biometric identification

Pengenalan Identitas Manusia Melalui Iris Mata Menggunakan Gray Level Co-occurence Matrix

Pada tugas akhir ini penulis menerapkan metode Gray Level Co-occurence Matrix sebagai ekstraksi ciri pada iris mata dan untuk mengklasifikasikan data menggunakan K-Nearest Neighbors serta Support Ve- ctor Machine. Dalam sistem ini menggunakan dataset UBIRIS Version 1 yang berjumlah 2000 gambar iris dari 100 individu. Hasil yang didapatkan dari pengujian ini menggunakan ekstraksi ciri Gray Level Co- occurence Matrix mampu menghasilkan F1 Score sebesar 71.10%

Iris recognition method based on segmentation

The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studie

Feature Matching in Iris Recognition System using MATLAB

Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software

Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition

An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints, and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition because iris can neither be surgically operated with a chance of losing slight nor change due to aging. However, presently most iris recognition systems available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with the existing method of iris recognition. This research investigates the possibility of developing a technique for recognition partially captured iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%, and 12.5% and to find a threshold for a minimum amount of iris region required to authenticate the individual. The research also developed and implemented two Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre wavelet filter is to enhance the feature extraction technique. Selected iris images from CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced technique. The technique was able to produce recognition accuracy between 70 – 90% CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with 74.95%, and MMU with 94.45%

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person