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

A Survey Paper on Palm Prints Based Biometric Authentication System

In this paper we are providing an approach for authentication using palm prints. Reliability in computer aided personal authentication is becoming increasingly important in the information-based world, for effective security system. Biometrics is physiological characteristics of human beings, unique for every individual that are usually time invariant and easy to acquire. Palm print is one of the relatively new physiological biometrics due to its stable and unique characteristics. The rich information of palm print offers one of the powerful means in personal recognition

Automated dental identification: A micro-macro decision-making approach

Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. In this work we consider three important problems in automated dental identification beyond the basic approach of tooth-to-tooth matching.;The first problem is on automatic classification of teeth into incisors, canines, premolars and molars as part of creating a data structure that guides tooth-to-tooth matching, thus avoiding illogical comparisons that inefficiently consume the limited computational resources and may also mislead the decision-making. We tackle this problem using principal component analysis and string matching techniques. We reconstruct the segmented teeth using the eigenvectors of the image subspaces of the four teeth classes, and then call the teeth classes that achieve least energy-discrepancy between the novel teeth and their approximations. We exploit teeth neighborhood rules in validating teeth-classes and hence assign each tooth a number corresponding to its location in a dental chart. Our approach achieves 82% teeth labeling accuracy based on a large test dataset of bitewing films.;Because dental radiographic films capture projections of distinct teeth; and often multiple views for each of the distinct teeth, in the second problem we look for a scheme that exploits teeth multiplicity to achieve more reliable match decisions when we compare the dental records of a subject and a candidate match. Hence, we propose a hierarchical fusion scheme that utilizes both aspects of teeth multiplicity for improving teeth-level (micro) and case-level (macro) decision-making. We achieve a genuine accept rate in excess of 85%.;In the third problem we study the performance limits of dental identification due to features capabilities. We consider two types of features used in dental identification, namely teeth contours and appearance features. We propose a methodology for determining the number of degrees of freedom possessed by a feature set, as a figure of merit, based on modeling joint distributions using copulas under less stringent assumptions on the dependence between feature dimensions. We also offer workable approximations of this approach

Combining multiple Iris matchers using advanced fusion techniques to enhance Iris matching performance

M.Phil. (Electrical And Electronic Engineering)The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images

Use of Multimodal Biometric System for the Authentication and Security

We know that security is the main challenge of the modern era, there may be many techniques and methods used for providing the security of any system or data. The biometric system is the widely used method for security. The biometric security is more secure method then others like passwords, card etc. In biometric the part of the body with some uniqueness use as the input image. But now a day the use of single biometric is not more secure, so the alternative or solution of this problem is to use the Multimodal biometric system. In a multimodal system more than one biometric trait used for providing security and authentication to a system. The images are stored in the database by using different operations on the images for compare it by input images. There are some steps used in processing of multimodal biometric system like as ROI, feature extraction and matching

Review of Multimodal Biometric Identification Using Hand Feature and Face

In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Security refers to prohibit some unauthorized persons from some important data or from some precious assets. So we need accurateness on automatic personal identification in various applications such as ATM, driving license, passports, citizen's card, cellular telephones, voter's ID card etc. Unimodal system carries some problems such as Noise in sensed data, Intra-class variations, Inter-class similarities, Non-universality and Spoof attacks. The accuracy of system is improved by combining different biometric traits which are called multimodal. This system gives more accuracy as it would be difficult for imposter to spoof multiple biometric traits simultaneously. This paper reviews different methods for fusion of biometric traits