Multimodal Biometric Verification System Research Articles

Performance Analysis of Developed Multimodal Biometric Identity Verification System

The bank client identity verification system developed in the course of the IDENT project is presented. The total number of five biometric modalities including: dynamic handwritten signature proofing, voice recognition, face image verification, face contour extraction and hand blood vessels distribution comparison have been developed and studied. The experimental data were acquired employing multiple biometric sensors installed at engineered biometric terminals. The biometric portraits of more than 10 000 bank clients were registered and stored in the database during the presented study and then verified experimentally. Problem- specific survey was done on the basis of questionnaires completed by the subjects in order to assess the look and feel of the developed biometric system as well as to collect opinions concerning its implementation in banking outlets. A discussion concerning the quality of registered data and results achieved in the study is included.

Multi-modal biometric system on various levels of fusion using LPQ features

In the proposed multimodal biometric verification system, the system is implemented on all levels of fusion strategies (i) fusion prior to matching (sensor level and feature level) and (ii) fusion post matching (score level and decision level), a binding required and a deserving step that outputs a reliable and robust biometric identification and verification systems. We have chosen benchmark databases for our experimentation and considered physiological modalities such as face, palmprint, finger knuckle print, handvein. The performance measures considered here are FAR (False Acceptance Rate) and FRR (False Rejection Rate). Extracting texture features from a well-known texture operator-LPQ (local phase quantization), we have performed sensor level fusion adopting HAAR wavelets, feature level fusion using Z-Score normalization, score level fusion employing simple sum rule and decision level fusion with AND rule. For the implemented biometric recognition system, score level fusion strategy outer performs than the other fusion techniques in terms of EER, yielding good verification rate on all benchmark threshold values (0.01%, 0.1%, 1%), with the GAR=100% at 1% FAR. The tabulated results of the experiments are visualized by BAR chart

Continuous Distribution Approximation and Thresholds Optimization in Serial Multi-Modal Biometric Systems

Multi-modal biometric verification systems use information from several biometric modalities to verify an identity of a person. The false acceptance rate (FAR)and false rejection rate (FRR) are metrics generally used to measure the performance of such systems.In this paper, we first approximate the score distributions of both genuine users and impostors by continuous distributions. Then we incorporate the exact expressions of the distributions in the formulas for the expected values of both FAR and FRR for each matcher. In order to determine the upper and lower acceptance thresholds in the sequential multi-modal biometric matching, we further minimize the expected values of FAR and FRR for the entire processing chain. We propose a non-linear bi-objective programming problem whose objective functions are the two error probabilities. We analyze the efficient set of the bi-objective problem, and derive an efficient solution as a best compromise between the error probabilities. Replacing the least squares approximation of the score distributions by a continuous distributionapproximation, this approach modifies the method presented in Stanojević et al. [15] (doi: 10.1109/ICCCC.2016.7496752) (a).The results of our experiments showed a good performance of the sequential multiple biometric matching system based on continuous distribution approximation and optimized thresholds.(a)Reprinted (partial) and extended, with permission based on License Number3938230385072 © [2016] IEEE, from "Computers Communications and Control (ICCCC), 2016 6th International Conference on".

Providing multimodal biometric authentication using five competent traits

Recognition accuracy of a single biometric authentication system is often much limited; hence, a multimodal biometric approach for identity verification is proposed. A new way of person authentication based on five-competent traits, namely, iris, ear, palm print, fingerprint and retina, is proposed in this paper. Each metric is analysed individually to get the matching scores from the corresponding feature sets. These scores are then combined using weighted sum fusion rule. In order to provide liveness verification for our authentication system, we employ the retinal blood vessel pattern recognition. To validate our approach, several experiments were conducted on the images obtained from five different datasets. The experimental results reveal that this multimodal biometric verification system is much more reliable and precise than the single biometric approaches.

Personal Identification from Lip-Print Features using a Statistical Model

This paper presents a novel approach towards identification of human beings from the statistical analysis of their lip prints. Lip features are extracted by studying the spatial orientations of the grooves present in lip prints of individuals using standard edge detection techniques. Horizontal, vertical and diagonal groove features are analysed using connected-component analysis to generate the region-specific edge datasets. Comparison between test and reference sample datasets against a threshold value to define a match yield satisfactory results. FAR, FRR and ROC metrics have been used to gauge the performance of the algorithm for real-world deployment in unimodal and multimodal biometric verification systems.

An intelligent multimodal biometric system for high security access

The interface of computer technologies and biology is having a huge impact on society. Human recognition technology research projects promises new life to many security consulting firms and personal identification system manufacturers. The physiological or biological features of an individual, called biometrics, are unique to each human and remain unaltered during a person's lifetime and give a promising solution for security management. This paper presents an intelligent multimodal biometric verification system for physical access control, based on fusion of iris, face and fingerprint patterns. Feature vectors are created independently for query images and are then compared with the enrolled templates of each biometric trait to compute the matching score. The final decision is made by fusion at this matching score level. The proposed system is designed to suit embedded solutions for high security access in pervasive environments using biometric features.

Reliability-Based Decision Fusion in Multimodal Biometric Verification Systems

We present a methodology of reliability estimation in the multimodal biometric verification scenario. Reliability estimation has shown to be an efficient and accurate way of predicting and correcting erroneous classification decisions in both unimodal (speech, face, online signature) and multimodal (speech and face) systems. While the initial research results indicate the high potential of the proposed methodology, the performance of the reliability estimation in a multimodal setting has not been sufficiently studied or evaluated. In this paper, we demonstrate the advantages of using the unimodal reliability information in order to perform an efficient biometric fusion of two modalities. We further show the presented method to be superior to state-of-the-art multimodal decision-level fusion schemes. The experimental evaluation presented in this paper is based on the popular benchmarking bimodal BANCA database.

Usability evaluation of multi-modal biometric verification systems

As a result of the evolution in the field of biometrics, a new breed of techniques and methods for user identity recognition and verification has appeared based on the recognition and verification of several biometric features considered unique to each individual. Signature and voice characteristics, facial features, and iris and fingerprint patterns have all been used to identify a person or just to verify that the person is who he/she claims to be. Although still relatively new, these new technologies have already reached a level of development that allows its commercialization. However, there is a lack of studies devoted to the evaluation of these technologies from a user-centered perspective. This paper is intended to promote user-centered design and evaluation of biometric technologies. Towards this end, we have developed a platform to perform empirical evaluations of commercial biometric identity verification systems, including fingerprint, voice and signature verification. In this article, we present an initial empirical study in which we evaluate, compare and try to get insights into the factors that are crucial for the usability of these systems.