Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

Speaker verification is desirable widely in many speech related applications, such as automatic telephone banking and biometric security system. This paper proposes an application of line spectrum frequency (LSF), formant, and support vector machine (SVM) to develop an algorithm of text-dependent speaker verification system. First, LSF and formant are extracted from the voiced password provided by the user. Then the proposed algorithm will make use of SVM to train the speaker characteristics from these speaker features and finally generate a claimed speaker model to discriminate between the speaker and other impostors. Experiments were conducted on the real speech signals and shown the performance of the proposed algorithm yields an equal error rate (EER) of 2.12% with 8-order LSFs and formant information. In addition, both of the false acceptance rate (FAR) and the false rejection rate (FRR) are also improved remarkably.

"This paper explores the application of neural networks in multimodal biometric authentication systems, emphasizing the integration of multiple biometric modalities to enhance security, accuracy, and robustness against various attacks. Traditional authentication methods, such as passwords and single-modal biometrics, often suffer from vulnerabilities, including spoofing, environmental factors, and data breaches. To address these challenges, multimodal authentication systems combine several biometric traits, such as facial recognition, fingerprint scanning, voice recognition, and keystroke dynamics, to achieve higher reliability and resistance to security threats. The study provides an overview of public datasets used for training neural network-based biometric authentication models, including VoxCeleb, RAVDESS, MOBIO, and SDUMLA-HMT. These datasets contain diverse biometric information necessary for developing robust multimodal authentication systems. The paper evaluates the effectiveness of existing approaches using key performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and area under the curve (AUC). Additionally, specialized metrics are considered, including failure to enroll rate (FTE), failure to acquire rate (FTA), and template stability (TS), which are crucial for real-world applications. The role of neural networks in multimodal biometric authentication is analyzed by examining state-of-the-art architectures, including convolutional neural networks (CNNs) and deep learning-based feature fusion methods. Various fusion levels—feature-level, score-level, and decision-level—are discussed to determine the optimal integration strategy for improving authentication performance. The results indicate that multimodal systems significantly outperform unimodal authentication methods by reducing vulnerability to spoofing and environmental variations. Experimental findings suggest that integrating multiple biometric traits enhances the system’s adaptability to dynamic conditions, reducing both false acceptance and false rejection rates. Despite these advantages, several challenges remain, including computational complexity, data privacy concerns, and the need for real-time processing capabilities. Future research should focus on optimizing multimodal fusion techniques, improving generalization across different datasets, and enhancing the security of biometric templates against adversarial attacks. Additionally, developing lightweight neural network architectures suitable for mobile and embedded systems is essential for the practical deployment of multimodal authentication technologies"

In recent years the security breaches and fraud transactions are increasing day by day. So there is a necessity for highly secure authentication technologies. The security of an authentication system can be strengthened by using Biometric system rather than the traditional method of authentication like Identity Cards (ID) and password which can be stolen easily. A biometric system works on biometric traits and fingerprint has the maximum share in market for providing biometric authentication as it is reliable, consistent and easy to capture. Although the biometric system is used to provide security to many applications but it is susceptible to different types of assaults too. Among all the modules of the biometric system which needs security, biometric template protection has received great consideration in the past years from the research community due to sensitivity of the biometric data stored in the form of template. A number of methods have been devised for providing template protection. Fuzzy vault is one of the cryptosystem based method of template security. The aim of fuzzy vault technique is to protect the precarious data with the biometric template in a way that only certified user can access the secret by providing valid biometric. In this paper, a modified version of fuzzy vault is presented to increase the level of security to the template and the secret key. The polynomial whose coefficients represent the key is transformed using an integral operator to hide the key where the key can no longer be derived if the polynomial is known to the attacker. The proposed fuzzy vault scheme also prevents the system from stolen key inversion attack. The results are achieved in terms of False Accept Rate (FAR), False Reject Rate (FRR), Genuine Acceptance Rate (GAR) by varying the degree of polynomial and number of biometric samples. It was calculated that for 40 users GAR was found to be 92%, 90%, 85% for degree of polynomial to be 3, 4 and 5 respectively. It was observed that increasing the degree of polynomial decreased the FAR rate, thus increasing the security

Genuine person recognition systems are necessary for a system to identify or authorize the identification of a person requesting their services. Ensuring that only authorized users access the provided services is the aim of this proposed system. These systems are susceptible to impersonation attacks in the absence of strong personal recognition methods. Due to its traditional authentication technique (PIN), the ATM has suffered greatly over the years from PIN theft and other related ATM thefts. Therefore, in this paper, we proposed a multilayer (Typing and Touch) based authentication security arrangement to enhance the security and safety for the ATM and its users. The proposed system demonstrates a threelayered design structure. The first layer is the card verification module, which concentrates on the enrolment phase for ATM card. The second layer is the feature extraction and comparing of the typing pattern from the database server. The last layer compares the touch behavior continue till session end with the stored touch data in the database server. During the implementation phase, MySQL server 2010 served as the backend and Java was used for front-end development on Microsoft Windows 10. Based on the False Rejection Rate (FAR), False Acceptance Rate (FAR), and Equal Error Rate (EER) measurements, the application was evaluated. These metrics demonstrate the security and dependability of the suggested system for the authentication and verification of ATM customers.

This chapter aims to enhance the security of textual passwords by adding a new layer that involves an individual's typing rhythm without requiring additional devices. It discusses authentication methods and develops a textual password authentication system, which takes advantage of the fact that every user has a distinct way of typing. Twenty participants, including novice and expert users, assessed the proposed system. The measuring metrics used were the False Acceptance Rate (FAR) and False Rejection Rate (FRR). The flight time and typing rhythm were the biometric identification template. The results indicated low false rejection and acceptance rates, with the initial session registering 0.0 FRR and 0.1 FAR. Two weeks later, the second session recorded 0.25 FRR and 0.0 FAR. Using this model, users do not need to select a complex password they might forget. Instead, they can utilize a good rhythm different from their natural typing rhythm, making it challenging to guess.

Problem statement: In any real time biometric system processing speed and recognition time are crucial parameters. Reducing processing time involves many parameters like normalization, FAR, FRR, management of eyelid and eyelash occlusions, size of signature etc. Normalization consumes substantial amount of time of the system. This study contributes for improved iris recognition system with reduced processing time, False Acceptance Rate (FAR) and False Rejection Rate (FRR). Approach: To improve system performance and reliability of a biometric system. It avoided the iris normalization process used traditionally in iris recognition systems. The technique proposed here used different masks to filter out iris image from an eye. Comparative study of different masks was done and optimized mask is proposed. The experiment was carried on CASIA database consisting of 756 iris images of 108 persons. Each person contributes seven images of eye (108×7 = 756) images in the database. Results: In the proposed method: (1) Normalization step is avoided; (2) Computational time is reduced by 0.3342 sec; (3) Iris signature size is reduced; (4) Improved performance parameters. (With reduced feature size, proposed method achieves 99.4866% accuracy, 0.0069% FAR, 1.0198% FRR and significant increase in speed of the system). Conclusion: Iris signature proposed was comparatively small just of 1×24 size. Though Daugman’s method gives best accuracy of 99.90% but the iris signature length used by that algorithm is comparatively very high that is 1×2048 phase vector. Also Daugman has used phase information in signature formation. Our method gives a accuracy of 99.474% with a signature of comparatively very small length. This has definitely contributed to improve the speed.

In biometric systems, reference threshold is defined as a value that can decide the authenticity of a person. Authenticity means whether the person is genuine or intruder. The statistical calculation of various values like reference threshold, FAR (False Acceptance Rate), FRR (False Rejection Rate) are required for real- time automated biometric authentication system because the measurement of biometric features are statistical values. In this paper, the need of reference threshold, how reference threshold value is calculated is explained mathematically. Various factors on which reference threshold value depends are discussed. It is also explained that how selection of correct value of reference threshold plays an important role in authentication system. Experimental results describe the selection of reference threshold value for palmprint biometric system.

Secure buildings are currently protected from unauthorized access by a variety of devices. Nowadays, there are many kinds of devices to guarantee the building security such as PIN pads, keys both conventional and electronic, identity cards, cryptographic and dual control procedures. However, these conventional devices can be stolen, forgotten, lost, guessed or impersonated with accuracy. Biometric system based on behavioral and/or physiological characteristics of person becomes popular as an alternative method to overcome the problem of conventional method. In this paper, voice-based biometric system is introduced for access control of building security. The ability to verify the identity of a person by analyzing his/her speech, or speaker verification, is an attractive and relatively unobtrusive means of providing security for admission into an important or secured place. An individualpsilas voice cannot be stolen, lost, forgotten, guessed, or impersonated with accuracy. In the proposed system, the access may be authorized simply by means of an enrolled user speaking into a microphone attached to the system. The proposed system then will decide whether to accept or reject the userpsilas identity claim or possibly to report insufficient confidence and request additional input before making the decision. Two approaches are adopted and evaluated to model the authorized persons, namely classical Gaussian mixture model (GMM) and artificial neural network (ANN). Experimental result confirms that in terms of false acceptance rate (FAR) and false rejection rate (FRR), the proposed voice-based access control with ANN model is better than that with GMM.

Today, numerous biometric systems have been suggested and created. Most used among them are face, fingerprint, and voice recognition. Each of them has their own advantages and disadvantages. Discussing about the automatic speaker verification (ASV), speech signal is a natural signal which one can easily obtain even from a telephone call. Research in this area is carried out from decades. This speaker verification technology has advanced in recent years and has become a genuine method for biometric systems. Using an adaptable Gaussian Mixture Model (GMM-UBM), a text-independent speaker verification (TISV) technique has been developed and compared with state-of-art I-vector based speaker recognition system in this research. Parameters for universal background model (UBM) are trained using the EM (Expectation maximization) and MAP adaptation method is used for training speaker models. Multiple false acceptance and false rejection rates are calculated by changing the threshold values for comparison. The results are shown in equal error rate (EER) for both the GMM-UBM, and I-vector based ASV systems, and the lowest EER is found to be 5.31% and 4.74%, respectively, after adjusting the threshold settings and number of Gaussian mixtures utilized.

Biometrics is used to identify individual in groups by their characteristics, such as fingerprint, face, iris, voice and behavioral characteristics like hand written signature and keystroke. Normally, a majority of biometrics recognition systems adopt methods by using unique feature to discriminate different individual from groups. But there is some forgery, subjective modifications like making up exiting as usual. A multimodal biometric recognition systemusing multiple biometrics to identify is proposed. In addition, it can support better Equal Error Rate (EER), False Acceptance Rate (FAR) and False Rejection Rate (FRR) by combining two or more physical or behavioral features. With lots of approached have been proposed, experts also pay much attention to the methods mentioned in recent years. The objective of this paper is to support a survey of multimodal biometric recognition system and approach using face, fingerprint recognition and enhanced iris characters that commonly used recently.

This paper proposes a small-footprint wake-up-word (WUW) recognition system for real noisy environments by employing the competing-words-based feature. Competing-words-based features are generated using a ResNet-based deep neural network with small parameters using the competing-words dataset. The competing-words dataset consists of the most acoustically similar and dissimilar words to the WUW used for our system. The obtained features are used as input to the classification network, which is developed using the convolutional neural network (CNN) model. To obtain sufficient data for training, data augmentation is performed by using a room impulse response filter and adding sound signals of various television shows as background noise, which simulates an actual living room environment. The experimental results demonstrate that the proposed WUW recognition system outperforms the baselines that employ CNN and ResNet models. The proposed system shows 1.31% in equal error rate and 1.40% false rejection rate at a 1.0% false alarm rate, which are 29.57% and 50.00% relative improvements compared to the ResNet system, respectively. The number of parameters used for the proposed system is reduced by 83.53% compared to the ResNet system. These results prove that the proposed system with the competing-words-based feature is highly effective at improving WUW recognition performance in noisy environments with a smaller footprint.

Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracyof 88 % ( ± 10 % SD) on two-way identification tests with 12 enrollment samples and accuracy of 80 % and an equal error rate (EER) of 20 % on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.