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Multimodal biometric authentication method can conquer the defects of the unimodal biometric authentication technology. In this paper, we design and develop an efficient Android-based multimodal biometric authentication system with face and voice. Considering the hardware performance restriction of the smart terminal, including the random access me...
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... deal with the problem of instability, increase the authentication accuracy and system reliability, we design and develop a novel efficient Android-based multimodal biometric authentication system by integrating the face and voice biometric features. Fig. 4 illustrates the flow chart of the developed system. Obviously, it can be considered as consisted of the following processes, i.e., face matching, voice matching, and fusion authentication. Next, each process will be separately discussed in ...
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... Android-based studio platform, and the SDK tool is used to compile and generate the dynamic link library. Finally, the Java client is used to generate the Android-based SDK, and Android-based application for the multimodal authentication system in apk format is encapsulated and generated. The flow chart of the specific process is illustrated in Fig. 14. In this subsection, we introduce our developed Androidbased multimodal authentication system. Fig. 15 (a) illustrates the main interface of the system. Since it is composed of the registration module and the identity authentication module, we will introduce them ...
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... However, relying solely on single biometric modalities for user authentication can lead to decreased trustworthiness owing to data corruption and adversarial attacks. To overcome the drawbacks of single-biometric authentication, multiple-biometric modalities can be leveraged to enhance the reliability of user authentication (Zhang et al., 2020). Multi-biometric authentication, which primarily uses fingerprint, palmprint, and iris information, offers alternative modalities when normal classification becomes difficult owing to adversarial attacks on specific biometric information or errors in measurement devices. ...
Research on multi‐biometric authentication systems using multiple biometric modalities to defend against adversarial attacks is actively being pursued. These systems authenticate users by combining two or more biometric modalities using score or feature‐level fusion. However, research on adversarial attacks and defences against each biometric modality within these authentication systems has not been actively conducted. In this study, we constructed a multi‐biometric authentication system using fingerprint, palmprint, and iris information from CASIA‐BIT by employing score and feature‐level fusion. We verified the system's vulnerability by deploying adversarial attacks on single and multiple biometric modalities based on the FGSM, with epsilon values ranging from 0 to 0.5. The experimental results show that when the epsilon value is 0.5, the accuracy of the multi‐biometric authentication system against adversarial attacks on the palmprint and iris information decreases from 0.995 to 0.018 and 0.003, respectively, and the f1‐score decreases from 0.995 to 0.007 and 0.000, respectively, demonstrating susceptibility to adversarial attacks. In the case of fingerprint data, however, the accuracy and f1‐score decreased from 0.995 to 0.731 and from 0.995 to 0.741, respectively, indicating resilience against adversarial attacks.
... Individual parameters including age, gender, location, size, and the heart's structure contribute to the unique characteristics of an electrocardiogram. This is why biometric recognition systems have started utilizing ECG signals [4]. The requirement that the signals be taken from a living organism is the primary benefit of ECG-based biometric identification systems in terms of security [5]. ...
... The use of multimodality in biometric authentication helps to circumvent the drawbacks of single-modality methods. An Android-based biometric identification system that uses both facial and vocal recognition was developed and implemented by Zhang et al. [4]. Considering the limitations of smart terminal hardware, such as random access memory (RAM), central processing unit (CPU), graphics processor unit (GPU), etc., which are unable to efficiently store and process large amounts of data, a face detection method is implemented to efficiently remove the redundant background of an image and minimize unnecessary information. ...
... The proposed model selects the most appropriate features used for accurate biometric model. The proposed model is compared with the traditional HLSTM model [1], Privacy-Preserving ECG-Based Authentication System for Securing Wireless Body Sensor Networks (PPECGbAS-WBSN) [2], and Android-based Multimodal Biometric Authentication System (AbMBAS) [4]. ...
Many societal institutions have increased standards for the efficacy and dependability of identification systems in response to the ever-increasing sophistication of computer technology. Biometric identity systems have mostly supplanted the usage of conventional key and smart card systems in highly secure industries. There has been a dramatic increase in the adoption of biometric authentication systems in the last decade. While many biometric systems have relied on exterior physiological traits like fingerprints, iris scans, palm prints, and faces, very few have investigated the possibility of using interior physiological traits as a biometric. It is challenging for the benefits of unimodal biometric technology to be realized in real-world applications because to its inherent constraints, such as the fact that it only proposes a single piece of information and that environmental factors can impact data verification. While passwords have served their purpose of authenticating users and controlling access, they have also revealed their weaknesses. Concerns about forging or impersonating authentication have prompted the proposal of various Artificial Intelligence (AI)-based solutions. A lot of people are interested in user authentication based on ECGs these days. Unlike other biometrics, an electrocardiogram verifies that the person is actual and alive, making it one of the most trustworthy advanced authentication methods. The P, Q, R, S, and T characteristic points of an ECG signal are responsible for its most salient features. Feature selection is a statistical method that can be implemented in a supervised or unsupervised setting via regression or classification. A Convolution Neural Network (CNN) is a type of neural network that extracts and feeds into another neural network, which then classifies those extracted features. The ECG signal serves as input to a feature extraction network. The neural network performs categorization based on the retrieved feature signals. To overcome the problem that the recognition accuracy of traditional ECG identification methods declines with the rise in the number of testing samples at various moments or throughout different heartbeat cycles, this research offers a multi feature reuse model. In this research, an Associated Priority-based Weighted Multi-Feature Vector model using Convolution Neural Network (APbWMFV-CNN) is proposed for ECG signal-based authentication. The proposed model selects the most appropriate features used for accurate biometric model. The comparison results represent that the proposed model performance in user detection is high.
... In recent years, the proliferation of biometric systems in sectors ranging from finance to law enforcement has raised concerns about the vulnerability of sensitive personal data [1]- [4]. Critics argue that the reliance on biometric identifiers, such as facial features, exposes individuals to heightened risks of identity theft and unauthorized surveillance [5]- [8]. Moreover, the potential for data breaches and misuse underscores the imperative for robust security protocols and stringent regulatory frameworks to safeguard privacy rights and mitigate potential abuses [9]- [14]. ...
This study investigates sentiment analysis methodologies within the framework of CRISP-DM (Cross-Industry Standard Process for Data Mining), aiming to discern the efficacy of various algorithms in sentiment classification tasks. The research uses a structured approach to evaluate SVM, NBC, DT, and K-NN algorithms with the SMOTE oversampling technique, uncovering distinct performance metrics and limitations. Results indicate SVM achieving 59.88% accuracy, NBC at 59.25%, DT with 52.09%, and K-NN obtaining 54.80%, highlighting the differential precision, recall, and f-measure. Additionally, content analysis identifies pertinent themes such as Biometric security, Cloud storage, and Emotion Analysis, enriching sentiment dynamics comprehension. The toxicity scores of analyzed videos reveal nuanced sentiment nuances, with the first video exhibiting Toxicity: 0.13227 and the second scoring Toxicity: 0.12794. This study underscores the significance of informed algorithm selection and evaluation methodologies within CRISP-DM, fostering optimized sentiment analysis outcomes while acknowledging diverse topical nuances.
... Face and voice recognition have gained significant attention among various biometric modalities due to their widespread availability and non-intrusive nature [1]. However, each modality has its limitations and vulnerabilities. ...
... Facial recognition systems can be susceptible to spoofing attacks using photographs or videos, while voice recognition systems can be compromised through voice synthesis techniques. To address these challenges, researchers have turned to multimodal biometric systems that combine face and voice to enhance security and reliability [2] and [1]. In this paper, we introduce an innovative multimodal Siamese network that seamlessly integrates face and voice biometrics, setting a new benchmark for efficiency in authentication systems. ...
... In [1], the researchers proposed a multi-biometric authentication system by integrating face and voice biometric information. The system demonstrates promising authentication performance surpassing existing methods through cross-validation experiments using XJTU databases and the face and voice database ( GT DB and TIMIT database). ...
Ensuring secure and reliable identity verification is crucial, and biometric authentication plays a significant role in achieving this. However, relying on a single biometric trait, unimodal authentication, may have accuracy and attack vulnerability limitations. On the other hand, multimodal authentication, which combines multiple biometric traits, can enhance accuracy and security by leveraging their complementary strengths. In the literature, different biometric modalities, such as face, voice, fingerprint, and iris, have been studied and used extensively for user authentication. Our research introduces a highly effective multimodal biometric authentication system with a deep learning approach. Our study focuses on two of the most user-friendly safety mechanisms: face and voice recognition. We employ a convolutional autoencoder for face images and an LSTM autoencoder for voice data to extract features. These features are then combined through concatenation to form a joint feature representation. A Siamese network carries out the final step of user identification. We evaluated our model’s efficiency using the OMG-Emotion and RAVDESS datasets. We achieved an accuracy of 89.79% and 95% on RAVDESS and OMG-Emotion datasets, respectively. These results are obtained using a combination of face and voice modality.
... Therefore, need of robust authentication system gained attention due their significant applications. In order to handle these issues, several researches have been presented which are mainly focused on developing the multimodal authentication system, for instance, Zhang et al. [9] presented a robust multimodal authentication system which is based on the voice and face data. Tarannum et al. [10] used a combination of IRIS, facial and fingerprint data to achieve the increased authentication performance. ...
... Zhang et al. [9] developed face and voice based multimodal authentication system. This model uses local binary pattern (LBP) method for face feature extraction and voice activity detection (VAD) for voice sample analysis to increase the voice detection accuracy. ...
... Where 0 denotes the central frequency. In next stage, we convert the polar coordinates to Cartesian coordinates, thus, the resulting frequency domain can be expressed as (9). ...
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
... However, research on feature fusion is limited, and it is constrained by single-modal feature extraction algorithms. Traditional feature fusion algorithms, such as serial feature fusion, parallel feature fusion, and fusion based on CCA and DCA/MDCA, have been proposed [15][16][17][18]. Feature fusion and feature extraction are the two key components of these methods. ...
The goal of information security is to prevent unauthorized access to data. There are several conventional ways to confirm user identity, such as using a password, user name, and keys. These conventional methods are rather limited; they can be stolen, lost, copied, or cracked. Because multimodal biometric identification systems are more secure and have higher recognition efficiency than unimodal biometric systems, they get attention. Singlemodal biometric recognition systems perform poorly in real-world public security operations because of poor biometric data quality. Some of the drawbacks of current multimodal fusion methods include low generalization and single-level fusion. This study presents a novel multimodal biometric fusion model that significantly enhances accuracy and generalization through the power of artificial intelligence. Various fusion methods, encompassing pixel-level, feature-level, and score-level fusion, are seamlessly integrated through deep neural networks. At the pixel level, we employ spatial, channel, and intensity fusion strategies to optimize the fusion process. On the feature level, modality-specific branches and jointly optimized representation layers establish robust dependencies between modalities through backpropagation. Finally, intelligent fusion techniques, such as Rank-1 and modality evaluation, are harnessed to blend matching scores on the score level. To validate the model’s effectiveness, we construct a virtual homogeneous multimodal dataset using simulated operational data. Experimental results showcase significant improvements compared to single-modal algorithms, with a remarkable 2.2 percentage point increase in accuracy achieved through multimodal feature fusion. The score fusion method surpasses singlemodal algorithms by 3.5 percentage points, reaching an impressive retrieval accuracy of 99.6%.
... The validation of data can present extraordinary issues with electronic correspondence, for example, weakness to man-in-the-center assaults, whereby an outsider takes advantage of the correspondence stream, and stances as every one of the two other imparting parties, to block data from each. Additional personality variables can be required to verify each gathering's character [17], [18]. ...
Extracting unique and distinctive traits is one of the most important challenges that researchers face, who rely on biometrics to extract exceptional traits for an individual. A large amount of biometric evidence that can be identified and found in various research has been done. In this paper, a biometrics system is proposed that combines the benefits of fingerprinting and uses a novel strategy to combine it with the image-based fingerprint vein feature set. The proposed system is fast and performs effective personal identification by combining both features. The features extracted from the venous print and fingerprint are matched to the nearest neighbors of the authorized person forms to verify the identity of the person. Several experiments have been performed on selected datasets to evaluate the performance of the new biometrics system. The obtained results prove that our proposed system is superior to biometric systems that use the feature of single biometrics. However, our goal is to set up an algorithm that is inexpensive in terms of time complexity while keeping it at the required security levels.
... Multimodal biometric authentication, as demonstrated by Zhang et al. [16], represents a promising area of research. Their study centers on creating an effective Android-based multimodal biometric authentication system through the integration of both facial and vocal recognition modalities, with the overarching goal being enhanced security compared to unimodal biometric authentication methods. ...
... While voice authentication continues to advance, current systems still face several challenges that need to be addressed. A major drawback is vulnerability to spoofing attacks using synthesized or replayed voice samples, as highlighted by Jiang et al. [6] and Zhang et al. [16]. Improving anti-spoofing and liveness detection remains an active area of research. ...
... To combat telecommunications fraud, a dual identity authentication mechanism has been proposed, combining the Hermes algorithm and voice-print recognition technology [154]. Multimodal biometric authentication, which includes face and voice recognition, provides better security [155]. Voice biometrics, however, are susceptible to replay attacks [156]. ...
This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecting biometric system accuracy and potential improvements. Our analysis delves into physiological and behavioral traits, exploring their pros and cons. We discuss factors influencing biometric system effectiveness and highlight areas for enhancement. Our study differs from previous surveys by extensively examining biometric traits, exploring various application domains, and analyzing measures to mitigate cyberattacks. This paper aims to inform researchers and practitioners about the biometric authentication landscape and guide future advancements.
... This method looks at the ridges and valleys on a person's fingertips. Due to its simplicity and non-intrusiveness, behavioral biometrics such as voice recognition, keyboard dynamics, and touchscreen interactions can offer a potential solution for user identification in online systems (Zhang et al., 2020). However, environmental factors such as noise, illness, or emotional state make them less accurate than physiological biometrics (Karim et al., 2020;Rui & Yan, 2019;Zhang et al., 2020). ...
... Due to its simplicity and non-intrusiveness, behavioral biometrics such as voice recognition, keyboard dynamics, and touchscreen interactions can offer a potential solution for user identification in online systems (Zhang et al., 2020). However, environmental factors such as noise, illness, or emotional state make them less accurate than physiological biometrics (Karim et al., 2020;Rui & Yan, 2019;Zhang et al., 2020). For the privacy and security of biometric authentication, it's crucial to ensure the storage and transfer of biometric data and individual training and awareness. ...
A robust authentication method is needed to protect online user accounts and data from cyberattacks. Using only passwords is insufficient because they can be easily stolen or cracked. Multi-factor authentication (MFA) increases security by requiring two or more verification factors from the user before granting access to a resource, such as an online account or an application. MFA is essential to a strong identity and access management (IAM) policy. This study evaluates and contrasts several MFA methods for online systems, including Microsoft Authenticator, FIDO2 security keys, SMS, voice calls, and biometrics. We assess these methods based on four criteria: security, usability, cost, and compatibility. We discover that only some MFA methods excel across the board. The best MFA method will depend on the organization's and users' specific needs and preferences. Each MFA method has benefits and drawbacks on its own. Based on our analysis, we do, however, make some general observations and recommendations, such as preferring FIDO2 security keys and certificate-based authentication for high-security scenarios, choosing Microsoft Authenticator and biometrics for high-usability scenarios, and avoiding SMS and voice calls for low-security and low-usability scenarios.
