BIOMETRIC AUTHENTICATION SYSTEM USING RPI

A biometric authentication system acquires biometric sample such as fingerprint. The fingerprint signifies physiological features of an individual.This is a system which maintains the attendance records of students automatically. In this designing of an efficient module that comprises of a fingerprint sensor to manage the attendance records of students. This module enrolls the student’s as well as staff’s fingerprints. This enrolling is a onetime process and their fingerprints will be stored in the fingerprint sensor. During enrolling of fingerprints alone requires a system since it is a onetime process. After enrolling process gets completed disconnect the module from the system and insert a battery into the module. This will provide power when the module is not connected with the system. The presence of each students will be updated in a database

Fast Iris Localization Based on Image Algebra and Morphological Operations

تحديد منطقة القزحية هي العملية الأكثر أهمية في نظام التعرف على القزحية التي تكون خاضعة وبشدة لتأثيرات البيئة,  وبالتالي، فقد تم اقتراح طريقة جديدة  لتحديد الحدود الداخلية والخارجية للقزحية. الفائدة الرئيسية من استخدام العمليات الحسابية للصور هي أنها طريقة بسيطة وسريعة وان هذه المميزات يتم استخدامها ودمجها مع العمليات المورفولوجية في تصميم الخوارزمية المقترحة. خوارزمية تحديد القزحية المقترحة قد صممت مع الأخذ بعين الاعتبار الملامح الشكلية لصورة قزحية العين مثل منطقة الضوضاء الموجودة في أجزاء مختلفة من صورة العين (مثل الانعكاسات الضوئية والتركيز والقزحية المرئية بصورة جزئية). النتائج التجريبية لتحديد القزحية تم إجراؤها على مجموعة من صور قزحية العين تتكون من 756 صورة تنتمي إلى قاعدة بيانات معهد العلوم الأكاديمي الصيني للأتمتة (CASIA V-1)، و450 صورة تنتمي إلى قاعدة بيانات جامعة الوسائط المتعددة (MMU V-1), تشير النتائج إلى تحقيق مستوى عالٍ من الدقة باستخدام التقنية المقترحة. علاوة على ذلك، فإن نتائج المقارنة مع خوارزميات تحديد القزحية الحديثة تعزز من دقة عملية فصل القزحية بشكل كبير اضافة الى كونها أكثر كفاءة من الناحية الحسابية.The localization of the iris is the most significant factor in biometrics of the iris, which traditionally assumes strictly controlled environments. The proposed method includes the pupillary and limbic iris boundaries localization. A primary advantage of image arithmetic operations is that the process is straightforward and therefore fast, these characteristics are employed and combined with the morphological operators in the designing of the proposed algorithm. The proposed algorithm takes into account the noise area which is found in various parts of the eye image such as light reflections, focus, and small visible iris. The experimental results are conducted on a collection of iris images consist of 756 images belong to Chinese Academy of Sciences Institute of Automation (CASIA V-1) and 450 images belong to Multi Media University (MMU V-1) databases.  The results indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris localization algorithms expose considerable improvement in segmentation accuracy while being computationally more efficient

Integrating simplified inverse representation and CRC for face recognition

© Springer International Publishing Switzerland 2015. The representation based classification method (RBCM) has attracted much attention in the last decade. RBCM exploits the linear combination of training samples to represent the test sample, which is then classified according to the minimum reconstruction residual. Recently, an interesting concept, Inverse Representation (IR), is proposed. It is the inverse process of conventional RBCMs. IR applies test samples’ information to represent each training sample, and then classifies the training sample as a useful supplement for the final classification. The relative algorithm CIRLRC, integrating IR and linear regression classification (LRC) by score fusing, shows superior classification performance. However, there are two main drawbacks in CIRLRC. First, the IR in CIRLRC is not pure, because the test vector contains some training sample information. The other is the computation inefficiency because CIRLRC should solve C linear equations for classifying the test sample respectively, where C is the number of the classes. Therefore, we present a novel method integrating simplified IR (SIR) and collaborative representation classification (CRC), named SIRCRC, for face recognition. In SIRCRC, only test sample information is fully used in SIR, and CRC is more efficient than LRC in terms of speed, thus, one linear equation system is needed. Extensive experimental results on face databases show that it is very competitive with both CIRLRC and the state-of-the-art RBCM

IRINA: Iris Recognition (even) in Inacurately Segmented Data

The effectiveness of current iris recognition systems de-pends on the accurate segmentation and parameterisationof the iris boundaries, as failures at this point misalignthe coefficients of the biometric signatures. This paper de-scribesIRINA, an algorithm forIrisRecognition that is ro-bust againstINAccurately segmented samples, which makesit a good candidate to work in poor-quality data. The pro-cess is based in the concept of ”corresponding” patch be-tween pairs of images, that is used to estimate the posteriorprobabilities that patches regard the same biological region,even in case of segmentation errors and non-linear texturedeformations. Such information enables to infer a free-formdeformation field (2D registration vectors) between images,whose first and second-order statistics provide effective bio-metric discriminating power. Extensive experiments werecarried out in four datasets (CASIA-IrisV3-Lamp, CASIA-IrisV4-Lamp, CASIA-IrisV4-Thousand and WVU) and showthat IRINA not only achieves state-of-the-art performancein good quality data, but also handles effectively severe seg-mentation errors and large differences in pupillary dilation/ constriction.info:eu-repo/semantics/publishedVersio

A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition

Biometric recognition is gaining attention as most of the organization is seeking for a more secure verification method for user access and other security application. There are a lot of biometric systems that exist which are iris, hand geometry and fingerprint recognition. In biometric system, iris recognition is marked as one of the most reliable and accurate biometric in term of identification. However, the performance of iris recognition is still doubted whether the iris recognition can generate higher accuracy when involving twin iris data. So, specific research by using twin data only needs to be done to measure the performance of recognition. Besides that, a comparative study is carried out using two template matching technique which are Hamming Distance and Euclidean Distance to measure the dissimilarity between the two iris template. From the comparison of the technique, better template matching technique also can be determined. The experimental result showed that iris recognition can distinguish twin as it can distinguish two different, unrelated people as the result obtained showed the good separation between intra and interclass and both techniques managed to obtain high accuracy. From the comparison of template matching technique, Hamming Distance is chosen as better technique with low False Rejection Rate, low False Acceptance Rate and high Total Success Rate with the value of 2.5%, 8.75% and 96.48% respectively

IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD