Multimodal Biometric Verification Research Articles

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models.

Multi-modal biometric fusion based continuous user authentication for E-proctoring using hybrid LCNN-Salp swarm optimization.

In Covid 19, pandemic remote proctoring of the employee or human being is evolved as a big challenge for the information retrieval process. On the other side, memory-based system access authentication is becoming outdated and less preferred for live applications, especially where data security and customer privacy are crucial. Multi-modal authentication has outperformed the unimodal process with high accuracy and improved security in the user authentication field. Multi-modal biometric verification includes user attributes such as keystrokes, iris, speech, face, etc. For real-time execution of multi-modal biometric fusion-based live tracking for compatible applications. The study proposes an efficient continuous biometric user authentication system for a new challenge of pandemic time, a live online authentication of the evaluation process (CBUA-OE). The proposed CBUA-OE system can address the challenges associated with live proctoring and is also compatible with real-time implementation, deployment of authentication systems. The modified wolf optimization algorithm and CUBA-OE's optimal feature fusion algorithm give an edge over the other contemporary methods and make it more robust. In modern forms of authentication, the classification stage affects the overall outcome of the system, and the model's performance is also a factor of varying quality of datasets. In contrast, a hybrid LCNN-Salp swarm optimization-based classifier is more efficient and consistent in continuous user authentication. Here the performance of the proposed hybrid LCNN-Salp swarm optimization classifier is analyzed with different standard datasets. The results are compared with the existing state-of-art classifiers regarding the accuracy, precision, recall, and F-measure. This projected work is novel in terms of usability factors and scalability to live tracking systems.

Intelligent multimodal identification system based on local feature fusion between iris and finger vein

<span>Biometric identification systems, which use physical features to check a person's identity, ensure much higher security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single modal biometric identification system fails to extract enough features for identification. Another disadvantage of using only one feature is not always readable. In this article, a smart multimodal biometric verification model for identifying and verifying a person's identity is recommended based on artificial intelligence methods. The proposed model is identified the iris and finger vein unique patterns each individual to overcome many challenges such as identity fraud, poor image quality, noise, and instability of the surrounding environment. Several experiments were performed on a dataset containing 50 people by using many matching methods. The results of the proposed model were provided a higher accuracy of 98%, with FAR and FRR of 0.0015% and 0.025%, respectively.</span>

Analysis of results of large‐scale multimodal biometric identity verification experiment

An analysis of a large set of biometric data obtained during the enrolment and the verification phase in an experimental biometric system installed in bank branches is presented. Subjective opinions of bank clients and of bank tellers were also surveyed concerning the studied biometric methods in order to discover and to explore relations emerging from the obtained multimodal dataset. First, data acquisition and identity verification methods are described in this study. Then, relationships between ratios of successful and failed verifications between pairs, triplets, and quartets of biometric modalities are studied. An analysis of the sentiment of clients and of banking tellers related to each identity verification attempt was performed based on linguistic methods. The data mining process is described, based on the rough sets methodology, aimed at deriving rules pertaining to consecutive identity verification attempts.

On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification

Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.

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

Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems

The paper proposes a likelihood ratio fusion of face, voice and signature multimodal biometrics verification application systems. Figueiredo-Jain (FJ) estimation algorithm of finite Gaussian mixture modal (GMM) is employed. Automated biometric systems for human identification measure a “signature” of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems. Simulation show that finite mixture modal (GMM) is quite effective in modelling the genuine and impostor score densities, fusion based the resulting density estimates achieves a significant performance on eNTERFACE 2005 multi-modal database based on face, signature and voice modalities.

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".

Figueiredo-Jain (FJ) Tune Algorithm for Gaussian Mixture Modal (GMM) Based Face and Signature Multi-Modal Biometric Verification Fusion Systems

Figueiredo-Jain (FJ) Tune Algorithm for Gaussian Mixture Modal (GMM) Based Face and Signature Multi-Modal Biometric Verification Fusion Systems

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.

A Novel Multimodal Biometric System based on Iris and Face

With the wide application, the recognition accuracy of unimodal biometric systems has to contend with a variety of problems such as background noise, signal noise and distortion, and environment or device variations. This paper combines face and iris features for developing a multimode biometric system, which is able to diminish the above mentioned problems of unimodal biometrics as well as to improve the performance of authentication system. We adopt an efficient feature-level fusion scheme for iris feature and face feature in series, and normalize the original features of iris and face using zscore model to eliminate the unbalance of magnitude and the distribution between two different kinds of feature vectors. Fisher discriminant analysis is used to select the distinguishing character of the fused feature in order to further enhance the performance of the system. The proposed algorithm is tested using CASIA iris database and two face databases (ORL database and Yale database). Experimental results reveal the multimodal biometrics verification is much more reliable and precise than unimodal biometric system.

Multimodal Biometrics for Improving Automatic Teller Machine Security

A multimodal biometrics face and fingerprint recognition system is a computer application for automatically identifying or verifying a person from face by using cameras and fingerprint by using sensors or fingerprint readers or fingerprint scanners. Proposed paper uses Face and Fingerprint recognition technique for verification in ATM systems. There are two types. The first one is verification. Compare the two faces and fingerprint images and decide whether the user (current user image) is an genuine user or imposter. These are decision level. Second one is identification this is where the system compares the given input image to all other images in the database and gives a ranked list of matches. Multimodal biometrics verification system that verifies the presence of a user is genuine or not. Two modalities are currently used—face and fingerprint—but our theory can be readily extended to include more modalities. We show that verification imposes additional requirements on multimodal fusion when compared to conventional verification systems. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for continuous verification and propose new metrics against which we benchmark our system.

How to handle missing data in robust multi-biometrics verification

Conventional multimodal biometrics systems usually do not account for missing modalities that is commonly encountered in real applications. In such cases, robust multimodal biometric verification is needed. In this paper, we present the criteria, fusion method and performance metrics of a robust multimodal biometrics verification system that verifies the client's identity at any condition of data missing. A novel adaptive Support Vector Machine (SVM) classification method is proposed for missing dimensional values. We argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for robust verification and propose new metrics against which we benchmark our system.

Multimodal Biometric Human Recognition for Perceptual Human–Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human-computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Multimodal person verification system using face and speech

Reliable user identification is a common requirement for almost every secure system. Biometric offer a natural and reliable solution to certain aspects of identity management by recognizing the individuals based on their inherent physical and behavior characteristics. Multimodal biometric person verification is gaining much popularity in recent years as they outperform unimodal person verification. This paper presents a person verification system using speech and face data. The verification system comprises of two classifiers whose scores are fused using sum rule after normalization. The experiments are carried out on VidTIMIT database. The experimental results show that face expert designed using Two-Dimensional Linear Discriminate Analysis and speech expert using Linear Prediction Cepstral Coefficients as feature extractor and Gaussian Mixture Model as opinion generator with 16 mixture will provide a Half Total Error Rate of 1.2%.

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.

Feature based RDWT watermarking for multimodal biometric system

This paper presents a 3-level RDWT biometric watermarking algorithm to embed the voice biometric MFC coefficients in a color face image of the same individual for increased robustness, security and accuracy. Phase congruency model is used to compute the embedding locations which preserves the facial features from being watermarked and ensures that the face recognition accuracy is not compromised. The proposed watermarking algorithm uses adaptive user-specific watermarking parameters for improved performance. Using face, voice and multimodal recognition algorithms, and statistical evaluation, we show that the proposed RDWT watermarking algorithm is robust to different frequency and geometric attacks, and provides the multimodal biometric verification accuracy of 94%.