Integrating Iris and Signature Traits for Personal Authentication Using User-SpecificWeighting

"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"

The traditional watermarking algorithms prove the rightful ownership via embedding of independent watermarks like copyright logos, random noise sequences, text etc into the cover images. Coupling biometrics with watermarking evolved as new and secure approach as it embeds user specific biometric traits and thus, narrows down the vulnerability to impostor attacks. A multimodal biometric watermarking system has been proposed in this paper in the redundant discrete wavelet transform(RDWT). Two biometric traits of the user i.e. the iris and facial features are embedded independently into the sub-bands of the RDWT of cover image taking advantage of its translation invariant property and sufficient embedding capacity. The ownership verification accuracy of the proposed system is tested based on the individual biometric traits as well as the fused trait. The accuracy was enhanced while using the fused score for evaluation. The security of the scheme is strengthened with usage of non-linear chaotic maps, randomization via Hessenberg decomposition, Arnold scrambling and multiple secret keys. The robustness of the scheme has been tested against various attacks and the verification accuracy evaluated based on false acceptance rate, false rejection rate, area under curve and equal error rate to validate the efficacy of the proposed scheme.

Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP) in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.

Review Paper on Applications of Principal Component Analysis in Multimodal Biometrics System

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

Unimodal biometric systems often face significant limitations due to sensitivity to noise intraclass variability and other factors. Multimodal biometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. However, the multimodal biometric system is limited to the time constraints due to its multiple processing stages. To overcome these limitations this paper presents a fast multimodal verification system by using the dynamic regions of the fingerprint image and enhanced iris segmentation method. The multimodal system is fused using rank level fusion at the verification stage. The performance of the biometric system shows improvement in the False Acceptance Rate (FAR) and Equal Error Rate (EER) curves. Furthermore, the time taken for the training and verification phase has a reduction of 15% when compared with the existing system tested on the slow processing mobile devices.

Since unimodal biometric systems based on single source of biometric information are always affected by problems such as noisy sensor data, no-universality and susceptibility to circumvention, using multimodal biometric systems that consolidate evidence from multiple biometric sources can remove some of these problems. In this paper, fingerprint, palm-print and hand-geometry are combined for person identity verification. Unlike other multimodal biometric systems, the user does not have to undergo the inconvenience of using two different sensors as three biometrics can be taken from the same image. Wavelet transform is employed to extract the features from fingerprint and palm-print and hand-geometry feature (such as width, length) is also extracted after the preprocessing phase. We employ feature fusion and mach score fusion together to establish identity. The system was tested on a database of 98 persons. The test performance results indicate the feasibility of the combination.

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

Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using an intelligent technique to authenticate human by fusion of palm and dorsal hand veins pattern. We developed an image analysis technique to extract region of interest (ROI) from palm and dorsal hand veins image. After extracting ROI we design a sequence of preprocessing steps to improve palm and dorsal hand veins images using Homomorphic, Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database chosen was the CASIA Multi-Spectral Palmprint Image Database V1.0 and Bosphorus Hand Vein Database. The achieved result for the fusion of both biometric traits was Correct Recognition Rate (CRR) is 97.6% with FAlse Reject Rate (FRR) 2.4%.KeywordsBiometric systemsPattern recognitionIntelligent computingImage processingMachine learning

Recent works have shown that multi-modal biometric systems are not robust against spoofing attacks [12, 15, 13]. However, this conclusion has been obtained under the hypothesis of a “worst case” attack, where the attacker is able to replicate perfectly the genuine biometric traits. Aim of this paper is to analyse the robustness of some multi-modal verification systems, combining fingerprint and face biometrics, under realistic spoofing attacks, in order to investigate the validity of the results obtained under the worst-case attack assumption.

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.

Biometric authentication is the process of recognizing a person by means of his\her psychological or behavioral traits. One of the most important issues faced by the biometric system developer is to protect the template obtained from the biometric of a person. Unimodal biometric system has some drawbacks such as noisy data, interclass variations and spoof attack. Multimodal biometric system has been developed to address the boundaries of unimodal biometric system and increase the security of template. In this paper, template security analysis of multimodal biometric system based of fingerprint and palmprint is proposed and implemented. Fuzzy vault scheme is employed to protect both the fingerprint and palmprint template. At enrollment, image processing techniques such as image enhancement, segmentation and bottom-hat filtering are applied on both the biometric to improve the quality and subsequently the most important features are extracted. Extracted features are concatenated. Combined features along with secret key are utilized to generate the database in the vault. During authentication, query images are sent as an input with the stored template to recover the key. Experimental results are shown that the proposed multi biometrics system performs well than the other methods considered for comparison.

A biometric system is an amalgamation of algorithms that uses an individual’s unique biological data innate physical features (fingerprints, retina, face, ears, etc.) or behavioural features (signature, voice, keystroke dynamics, etc.) or both to authenticate the individual. This essentially makes a biometric system function as a pattern recognition system that helps an individual in any kind of adversarial environment. Hence, like any conventional authentication system, it is vulnerable to the attacks of malicious adversaries that try to manipulate the data and thus decline the security of the system by compromising its robustness. These possible attacks have not been considered while developing most biometric authentication systems. Our work describes multimodal biometrics, its feature extraction methods, research challenges and focuses on the major security issues in both unimodal and multimodal biometric systems. This paper summarizes some robust multimodal systems, their countermeasures and algorithms that have been devised to tackle these security challenges.KeywordsBiometricsMultimodal biometricsBiometric fusionSecurityAttacksCountermeasuresEncryption

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.

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.