In this study, the application of adaptive Bloom filters to binary iris biometric feature vectors, that is, iris-codes, is proposed. Bloom filters, which have been established as a powerful tool in various fields of computer science, are applied in order to transform iris-codes to a rotation-invariant feature representation. Properties of the proposed Bloom filter-based transform concurrently enable (i) biometric template protection, (ii) compression of biometric data and (iii) acceleration of biometric identification, whereas at the same time no significant degradation of biometric performance is observed. According to these fields of application, detailed investigations are presented. Experiments are conducted on the CASIA-v3 iris database for different feature extraction algorithms. Confirming the soundness of the proposed approach, the application of adaptive Bloom filters achieves rotation-invariant cancellable templates maintaining biometric performance, a compression of templates down to 20-40% of original size and a reduction of bit-comparisons to less than 5% leading to a substantial speed-up of the biometric system in identification mode.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2013.0049

On application of bloom filters to iris biometrics

Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation approach, named IrisParseNet . Different from many previous CNN-based iris segmentation methods, which only focus on predicting accurate iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging iris databases, i.e., CASIA.v4-distance, UBIRIS.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed iris segmentation method can be used for any iris recognition methodology, and would significantly improve the performance of non-cooperative iris recognition.

Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition

IREX VI : Temporal Stability of Iris Recognition Accuracy

This paper discusses an analysis of human iris patterns for recognition of biometric system which consists of a segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region is then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. To encode the unique pattern of the iris into a bit-wise biometric template, 1D Log-Gabor filter is used.Finally to match two iris templates hamming distance is used as matching metric. The system performance is analyzed on 312 iris images taken from standard CASIA Iris Interval database version 4. To establish the verification accuracy of iris representation and matching approach, each iris image in the database is matched with all the other iris images in the database and genuine and imposter distribution is found .The performance of the system is implemented by evaluating the Decidability Index (DI), False match rate (FMR), False Non-match rate (FNMR), Genuine Accept Rate (GAR) and Equal error rate (EER).

/pdf/biometric-personal-identification-based-on-iris-patterns-46fmph1ree.pdf

Biometric Personal Identification based on Iris Patterns

Iris recognition using multiscale morphologic features

Iris recognition is an efficient method for identification of persons. This paper provides a technique for the iris recognition. A biometric system provides automatic identification of individuals. Iris is an unique feature which is applicable for identification. Unlike face recognition or finger prints, iris recognition comes from randomly distributed features. It is most reliable and accurate method of identification. This paper proposes a personal identification using iris recognition system with the help of six major steps i.e. image acquisition,localization, Isolation, normalization, feature extraction and matching and also these six steps consists a numbers of minor steps to complete each step. The boundaries of the iris, as papillary and limbic boundary, are detected by using Canny Edge Detector. We can use masking technique to isolate the iris image form the given eye image, this isolated iris image is transformed from Cartesian to polar co-ordinate. Now finally extract the unique features (feature vector) of the iris after enhancing the iris image and then perform matching process on iris code using Hamming Distance for acceptance and rejectance process. Keywords: Canny edge detection , Hamming distance , Hough transform, Masking, Histogram equalization, Haar wavelet.

/pdf/recognition-of-human-iris-patterns-for-biometric-yk6f1wczgv.pdf

K. V. S. Sravanthi-TeamLeader

Papers

Recognition of Human Iris Patterns for Biometric Identification