Multiple Biometric Modalities Research Articles

Construction of Athletes' Physical Condition Monitoring and Analysis System Using Biometrics Recognition Technology

Biometrics recognition technology utilizes unique biological characteristics such as fingerprints, iris patterns, facial features, or voiceprints to identify and authenticate individuals. Through advanced algorithms and pattern recognition techniques, biometric systems capture and analyze these physiological or behavioral traits to verify a person's identity. This technology offers high levels of security and accuracy, making it valuable for various applications including access control, time and attendance tracking, border security, and digital payments. The construction of an athletes' physical condition monitoring and analysis system utilizing biometric recognition technology represents a significant advancement in sports science. By integrating cutting-edge biometric sensors and recognition algorithms, this system provides real-time insights into athletes' physiological parameters and performance metrics. Athlete performance monitoring and analysis play pivotal roles in optimizing training strategies, preventing injuries, and enhancing overall athletic performance. In this paper, we propose a novel approach leveraging the Hybrid Multi-Instance Ensemble Classifier (HMIEC) combined with biometric recognition technology to accurately classify athlete data and assess their physical condition. Our study explores the effectiveness of HMIEC across multiple biometric modalities, including ECG-based, fingerprint-based, and facial recognition, in identifying athletes and monitoring their physical parameters. Through a series of experiments and analyses, demonstrate HMIEC's superior classification performance compared to other classifiers, with high accuracy, sensitivity, specificity, and area under the curve (AUC). The reductions in heart rate from 75 to 65 beats per minute, increase in oxygen saturation levels from 98% to 99%, decreases in blood pressure readings from 120/80 mmHg to 110/70 mmHg, and enhancements in flexibility from 30 to 35 centimeters, muscle strength from 100 lbs to 120 lbs, and endurance capacity from 20 to 25 minutes.

SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.

Multimodal Biometrics for Human Identification usingArtificial Intelligence

Multimodal biometric systems combine multiple biometric modalities to enhance the accuracy and security of human identification. Instead of relying on a single biometric trait (such as fingerprint or face), these systems use a combination of different biometric characteristics to provide a more robust and reliable identification process. The key idea behind multimodal biometrics is that the fusion of diverse biometric data can overcome the limitations of individual modalities, resulting in higher accuracy and lower error rates.

Face-Periocular Cross-Identification via Contrastive Hybrid Attention Vision Transformer

Traditional biometrics identification performs matching between probe and gallery that may involve the same single or multiple biometric modalities. This paper presents a cross-matching scenario where the probe and gallery are from two distinctive biometrics, i.e., face and periocular, coined as face-periocular cross-identification (FPCI). We propose a novel contrastive loss tailored for face-periocular cross-matching to learn a joint embedding, which can be used as a gallery or a probe regardless of the biometric modality. On the other hand, a hybrid attention vision transformer is devised. The hybrid attention module performs depth-wise convolution and conv-based multi-head self-attention in parallel to aggregate global and local features of the face and periocular biometrics. Extensive experiments on three benchmark datasets demonstrate that our model sufficiently improves the performance of FPCI. Besides that, a new face-periocular dataset in the wild, the Cross-modal Face-periocular dataset, is developed for the FPCI models training.

A Feature-Level Fusion Scheme Based on Eigen Theory for Multimodal Biometrics

ABSTRACT Multimodality is a promising trend to gain the reliability for biometrics. This paper presents a novel multimodal biometric recognition with information fusion at feature level, called eigen-based feature-level fusion (E-FLF) scheme, for personal authentication. The kernel idea of the proposed E-FLF scheme is to perform the eigen analysis for finding an optimum projection on the feature's cross-energy space by maximizing cross-energy ratio in the new projection space. Simple local and global features extracted from multiple biometric modalities, such as iris, palmprint and face, are considered in this fusion scheme. Different modes of fusion have been implemented to verify the validation of the proposed method. Experimental results reveal that the proposed fusion scheme for fusing features from multimodal biometric traits may improve the recognition performance significantly.

Iris-Fingerprint multimodal biometric system based on optimal feature level fusion model

<abstract><p>For reliable and accurate multimodal biometric based person verification, demands an effective discriminant feature representation and fusion of the extracted relevant information across multiple biometric modalities. In this paper, we propose feature level fusion by adopting the concept of canonical correlation analysis (CCA) to fuse Iris and Fingerprint feature sets of the same person. The uniqueness of this approach is that it extracts maximized correlated features from feature sets of both modalities as effective discriminant information within the features sets. CCA is, therefore, suitable to analyze the underlying relationship between two feature spaces and generates more powerful feature vectors by removing redundant information. We demonstrate that an efficient multimodal recognition can be achieved with a significant reduction in feature dimensions with less computational complexity and recognition time less than one second by exploiting CCA based joint feature fusion and optimization. To evaluate the performance of the proposed system, Left and Right Iris, and thumb Fingerprints from both hands of the SDUMLA-HMT multimodal dataset are considered in this experiment. We show that our proposed approach significantly outperforms in terms of equal error rate (EER) than unimodal system recognition performance. We also demonstrate that CCA based feature fusion excels than the match score level fusion. Further, an exploration of the correlation between Right Iris and Left Fingerprint images (EER of 0.1050%), and Left Iris and Right Fingerprint images (EER of 1.4286%) are also presented to consider the effect of feature dominance and laterality of the selected modalities for the robust multimodal biometric system.</p></abstract>

Multi-Biometric Identification With Cascading Database Filtering

The growing scale and number of biometric deployments around the world necessitates research into technologies which facilitate fast identification queries and high discriminative power. In this context, this article presents a biometric identification system which relies on a successive pre-filtering of the potential candidate list using multiple biometric modalities, coupled with a weighted score-level information fusion. The proposed system is evaluated in a series of experiments using a compound dataset constructed from several publicly available datasets; an open-set identification scenario is considered with the enrolment database containing 1,000 chimeric instances. This evaluation shows that the proposed system exhibits a significantly increased biometric performance w.r.t. a weighted score-level or rank-level fusion based baseline, while simultaneously providing a consequential computational workload reduction in terms of penetration rate. Lastly, it is worth noting that the proposed system could be flexibly employed in any multi-biometric identification system, irrespective of the chosen types of biometric characteristics and the encoding of their extracted features.

Multimodal Biometrics Recognition from Facial Video with Missing Modalities Using Deep Learning

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, nonplanar movement, and pose variations present in the video clips even in the situation of missing modalities.

Eigen-based binary feature amalgamation in multimodal biometrics

In this paper, a quantised eigen analysis (QEA) for the extracted features is proposed and an associated eigen-based binary feature amalgamation (EBFA) based on QEA is developed for feature fusion in multimodal biometrics. As opposed to feature combination, EBFA projects heterogeneous features onto the projection kernel and uses only the sign parts to encode the features as bit strings to maximise its expression rather than directly combine them. Thus, the feature codes can be simply concatenated or compared by XOR bit-wise operation into a serial or parallel amalgamated feature vector. To evaluate the performance of EBFA, a series of experiments are performed on multiple biometric modalities, including face, palm-print and iris. The experimental results show that the proposed binary feature amalgamation scheme at feature-level is superior to some other feature fusion methods and score-level methods in terms of multimodal recognition accuracy performance.

Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics

Information fusion is a challenging problem in biometrics, where data comes from multiple biometric modalities or multiple feature spaces extracted from the same modality. Learning from heterogeneous data sources, in general, is termed as multi-view learning, where view is an encompassing term that refers to different sets of observations having distinct statistical properties. Most of the existing approaches to learning from multiple views either assume that the views are either independent or fully dependent. However, in real scenarios, these assumptions are almost never truly satisfied. In this work, we relax these assumptions. We propose a feature fusion method called Discriminative Factorized Subspaces (DFS) that learns a factorized subspace consisting of a single shared subspace (that captures the common information), and view-specific subspaces that captures information specific to each view. DFS jointly learns these subspaces, by posing the optimization problem as a constrained Rayleigh Quotient based formulation, whose solution is efficiently obtained using generalized eigenvalue decomposition. Our method does not require lots of data to learn from, and we show how it is apt for domains characterized by limited training data, and high intra-class variability. As an application, we tackle the challenging problem of touchscreen biometrics, which is based on the study of user interactions with their touch screens. Through extensive experimentation and thorough evaluation, we demonstrate how DFS learns a better discriminatory boundary, and provides a superior performance than state of the art methods for touchscreen biometric verification.

Eigen-based binary feature amalgamation in multimodal biometrics

In this paper, a quantised eigen analysis (QEA) for the extracted features is proposed and an associated eigen-based binary feature amalgamation (EBFA) based on QEA is developed for feature fusion in multimodal biometrics. As opposed to feature combination, EBFA projects heterogeneous features onto the projection kernel and uses only the sign parts to encode the features as bit strings to maximise its expression rather than directly combine them. Thus, the feature codes can be simply concatenated or compared by XOR bit-wise operation into a serial or parallel amalgamated feature vector. To evaluate the performance of EBFA, a series of experiments are performed on multiple biometric modalities, including face, palm-print and iris. The experimental results show that the proposed binary feature amalgamation scheme at feature-level is superior to some other feature fusion methods and score-level methods in terms of multimodal recognition accuracy performance.

Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT

With the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, and so forth. In this paper, we discuss the cost versus the utility of biometric systems and investigate frameworks for improving them. We propose the noise-aware biometric quantization framework (NA-IOMBA) capable of generating unique, reliable, and high entropy keys with low enrollment times and costs with several experiments. First, we compare its performance with IOMBA and one-class-SVM on multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that NA-IOMBA outperforms them all and that ECG provides the best trade-off between reliability, key length, entropy, and implementation cost. Second, we examine the impact on key reliability with ECGs obtained at different sessions and trained with a different number of heartbeats. Finally, implementation results show that incorporating noise models with NA-IOMBA reduces power and utilization overhead by more than 60% by adapting the pre-processing, feature extraction, and post-processing modules.

A Multi-Biometric System Based on Feature and Score Level Fusions

In general, the information of multiple biometric modalities is fused at a single level, for example, score level or feature level. The recognition accuracy of a multimodal biometric system may not be improved by carrying fusion at a single level, since one matcher may provide a performance lower than that provided by other matchers. In view of this, we propose a new fusion scheme, referred to as the matcher performance-based (MPb) fusion scheme, in which the fusion is carried out at two levels, feature level, and score level, to improve the overall recognition accuracy. First, we consider the performance of the individual matchers in order to find out which of the modalities should be used for fusion at the feature level. Then, the selected modalities are fused at this level by utilizing their encoded features. Next, we fuse the score obtained from the feature-level fusion with that of the modality for which the performance is the highest. In order to carry out this fusion, a new normalization technique referred to as the overlap extrema-variation-based anchored min-max (OEVBAMM) normalization technique, is also proposed. By considering three modalities, namely, fingerprint, palmprint, and earprint, the performance of the proposed fusion scheme as well as that of the single level fusion scheme, both with various normalization and weighting techniques are evaluated in terms of a number of metrics. It is shown that the multi-biometric system based on the proposed fusion scheme provides the best performance when it employs the new normalization technique and the confidence-based weighting (CBW) method.

Entropy based Local Binary Pattern (ELBP) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage

Cloud Computing (CC) is a technology that is growing by leaps and bounds and has attracted wide spectrum of users. The extensive usage of cloud technology is influenced by multiple factors like ease of use, pay-per usage strategy, easy access, cost-effectiveness etc. Though it is a widely used technology, challenges exist in the form of security threats. There are a variety of services that are offered by cloud. These include Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). Storage is one of the key service offerings under IaaS. To provide a secure digital platform for users to work with, this research work proposes a novel security architecture for secured storage in cloud that provides a robust authentication by employing multiple biometric modalities from users and allow/deny access accordingly. The crux of better authentication relies on the way the features are extracted from multiple biometric sensors and matched with registered users. For this purpose, a novel feature extraction technique is proposed in this research work. Entropy Based Local Binary Pattern (ELBP) is a new texture-based feature extraction technique proposed to describe the entropy information into Local Binary Pattern histogram in one-dimensional space. ELBP feature extraction technique needs no quantization. Biometric images exhibit higher uniqueness and hence incorporating entropy values into local regions add higher information content to these images, thus leading to better feature extraction. The experiments are performed on biometric images from Chinese Academy of Science, Institute of Automation (CASIA) Iris, Face and Fingerprint databases and the results show that the proposed ELBP feature extraction achieves substantial improvement, in terms of various classification metrics like accuracy, precision, recall etc. in comparison with the conventional rotation invariant LBP methods. The Receiver Operating Characteristics Curve (ROC) also bears testimony to the performance of the authentication system.

Score-level fusion for cancelable multi-biometric verification

Integration of scores from multiple biometric modalities has become promising to alleviate the limitations of unibiometric systems such as sensitivity to outliers, erroneous authentication caused by inter-class and intra-class variability and low verification performance due to poor quality. In this work, we propose a two-level score level fusion approach for integrating the scores obtained from cancelable templates of different biometric modalities. As a result, we achieve a significant improvement in overall recognition performance providing secure authentication for the different application. At the first level, scores from multiple matchers are combined using a novel mean-closure weighting (MCW) technique to achieve the desired score for a particular biometric modality. The proposed solution is based on the region of uncertainty between the genuine and imposter distribution. Further, the derived scores from different modalities are integrated using a novel rectangular area weighting (RAW) technique at the second level to obtain the overall fused score. Overall, the proposed two-level cancelable score fusion method improves the performance over unimodal cancelable systems and are more robust to the variability of scores and outliers. The evaluation has been performed on two virtual databases and is compared with the existing weighting, density and classification based score fusion techniques. Experimental results show that the proposed two-level cancelable score fusion improves the overall performance over unibiometric system satisfying the requirement of secure authentication.

Multimodal biometric hashkey cryptography based authentication and encryption for advanced security in cloud

Cloud Computing (CC) is a technology that is growing at a faster pace, gaining much popularity with startup companies increasing and opting for clouds’ services. The growth rate of this technology is high and so is the security threat. Though there are existing authentication systems like two factor authentication, biometric based authentication and so on, hackers still pose greater challenges. Though password-based authentication system is simple, it is highly vulnerable to security breaches. Biometric based authentication has better security, but usage of single biometric traits can also be easily compromised. Thus, this research work, Multimodal Biometric Hashkey Cryptography (MBHC) brings about a novel security framework that focuses on both authentication and securing cloud data using multiple modalities. Authentication is based on multiple biometric modalities viz. Fingerprint, Iris and Face features. Features from these modalities are extracted using linear filters. Artificial Fish Swarm Algorithm (AFSA) is used for feature optimization, further to which Support Vector Machine (SVM) acts as classifier for identifying genuine users against imposters. Cryptographic keys are generated from these modalities that serve as input to Advanced Encryption Standard (AES) Algorithm for encrypting and decrypting the information stored in cloud. Integrity of data is also ensured using hash function for which the facial features and the encrypted message are the inputs. Thus, the experimental results show that the proposed architecture provides improved security for cloud computing environment when compared to the existing models.

Fusion in Multimodal Biometric System: A Review

Objectives: The use of multimodal biometric has been introduced recently owing to use of multiple biometric modalities. Here we perform in-depth review of the various methods used for multimodal biometric technology. Methods/ Statistical Analysis: Here we present a systematic review of various methods used for fusing multiple biometric modalites. Specifically, fusing at various levels such as, before matching and after matching. Score level, feature level, rank level and decision fusion is followed by feature optimization using methods such as genetic algorithms and artificial neural networks. Findings: Single biometric based methods suffer from lack of security and efficiency. This leads to advent of multimodal biometric systems. However, fusing various biometric modalities is being persued with very high interest. We describe the granular nature of several methods used to fuse multiple biometric modalities. A wide range of methods are being employed to fuse biometric data. These methods vary in efficiency and are highly dependant upon the selection of type of biometric chosen for fusion. Application/Improvements: As computational efficiency increases, there increase in more secure and efficient biometric systems that use multiple sources of biometric identification and access authorization.

Multimodal Biometric Technology Using Fuzzy Logic Decision and Fuzzy Inference System

In this research paper I discuss about the challenges to implement secure personal identification protocols with biometric technology are increasing and the need for accurate human identification is higher than ever. Single modality biometric systems have to contend with a variety of problems such as noisy data, intra class variations, non-universality, spoof attacks, and distinctiveness. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate multiple biometric modalities in a single scan to alleviate the challenges of a unimodal system. Performance in biometric verification is often affected by external conditions and variabilities. These are often related to mismatched conditions between enrolment and verification sessions, e.g. handsets/microphones for recording speech, cameras for capturing facial images and fingerprint readers. In addition, the user’s speech may vary according to ambient noise conditions, the speaker’s health (e.g. contracting a cold) or speaking styles. The user’s facial images may vary due to changes in backgrounds, illumination, head positions and expressions. While none of the biometrics alone can guarantee absolute reliability, they can reinforce one another when used jointly to maximize verification performance. This motivates multi-biometric (multimodal) authentication.

Cancelable features using log-Gabor filters for biometric authentication

Wide spread use of biometric based authentication requires security of biometric data against identity thefts. Cancelable biometrics is a recent approach to address the concerns regarding privacy of biometric data, public confidence, and acceptance of biometric systems. This work proposes a template protection approach which generates revocable binary features from phase and magnitude patterns of log-Gabor filters. Multi-level transformations are applied at signal and feature level to distort the biometric data using user specific tokenized variables which are observed to provide better performance and security against information leakage under correlation attacks. A thorough analysis is performed to study the performance, non-invertibility, and changeability of the proposed approach under stolen token scenario on multiple biometric modalities. It is revealed that generated templates are non-invertible, easy to revoke, and also deliver good performance.

Ford files patent to unlock car with biometrics routed through smartphone

The Ford motor car company has filed a patent for a system that enables a driver to use multiple biometric modalities to unlock their car.