Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1783-1788· 0 citations· 20 references
Abstract
The widespread use of biometric identification systems has raised important issues regarding template security and scalability. In contrast to passwords, biometric characteristics, once exposed, cannot be withdrawn, thus raising permanent risks of identity exposure. Moreover, traditional systems require complete retraining when adding new users, thus causing computational inefficiency and low scalability. This work presents an original framework for cancelable multi-biometric identification that combines face and iris dynamics in an incremental deep learning model. The proposed method produces non-invertible and revocable templates through deep feature extraction by ResNet-50, followed by a random projection transformation, thus meeting the ISO/IEC 24745:2022 security requirements. A dynamic 1D-CNN model, enhanced by elastic weight consolidation and rehearsal learning, enables incremental learning without catastrophic forgetting or complete model retraining. Experimental evaluation shows an average recognition rate of 98.98% on incremental datasets, with false acceptance rates as low as 0.115% and true acceptance rates of 98.93%. The proposed framework provides an optimal trade-off among security, scalability, and recognition performance, thus filling the most important gaps in current biometric systems and offering a basis for the development of a next-generation privacy-preserving identification infrastructure.
The increasing need for reliable human identification systems in secure environments has exposed the limitations of unimodal biometric systems. Individual biometric modalities, including fingerprint, face recognition, and iris scanning, are prone to noise, hardware limitations, spoofing attacks, and data acquisition errors, resulting in high false acceptance and false rejection rates. This paper presents a comprehensive framework for human identification using multi-biometric fusion of iris and face dynamics using a deep learning paradigm. By holistically combining the noise-free properties of the iris with the nonintrusive and globally accessible properties of facial features, the proposed system overcomes the fundamental limitations of unimodal biometric systems while harnessing their complementary benefits. The framework encompasses systematic data acquisition in various environmental settings, sophisticated preprocessing techniques involving normalization and augmentation, robust feature extraction using convolutional neural networks, and multi-level fusion techniques at the feature, score, and decision levels. Experimental results show that the proposed fusion strategy outperforms unimodal biometric systems in recognition accuracy, with a rate of accuracy above 99% and lower error rates. The proposed system is designed for critical applications in banking security, border protection, healthcare, and law enforcement, where identification accuracy and system integrity are paramount.
Shaik Badulla, Vundavalli Balasankar, G. L. V. Prasad· 2026 7th International Confe...· 0 citations
User authentication plays an essential role in security assurance in the present-day digital world. Traditional password-based authentication approaches are increasingly becoming vulnerable to attacks, while single modal biometric systems suffer from challenges such as noise susceptibility, deception, and intra-class variations. This paper proposes a secure and reliable multimodal biometric system using fingerprints, facial characteristics, and irises using Convolutional Neural Network (CNN) to address these weaknesses. In this system, self-learning CNN based representations are utilized for every biometric characteristic, allowing for automatic extraction of distinct deep representations without hand-crafted features. The obtained confidence or attributes across different biometrics are merged to create a combined representation for authentication. Confidence-based classification approach is utilized for authenticating the genuine and impersonator user. Efficiency of the proposed system has been tested using unique assessment criteria including accuracy, recall, true negative rate, false positive rate, precision, and false negative rates. Experimental outcomes demonstrate that the presented CNN-based multimodal biometric system achieves superior accuracy and robustness compared to the conventional single-modal systems and thus it can be used for practical security applications where reliability is required.
Samatha J· Journal of Intelligent Decis...· 0 citations
Biometric authentication is commonly adopted because biometric traits are unique and difficult to copy. Fingerprint recognition, in particular, is widely used in many practical systems, but privacy remains a serious concern. Many existing fingerprint systems store biometric templates in a form that can be misused if leaked. In these situations, attackers may reconstruct or reverse the data, revealing sensitive personal information. Since biometric characteristics cannot be replaced like passwords, any compromise may have long-term consequences. For this reason, privacy-preserving biometric designs are increasingly necessary. This study presents a cancelable fingerprint recognition system that combines Convolutional Neural Networks (CNNs) and Support Vector Regression (SVR). The CNN part of the system is utilized to learn the distinctive features of the fingerprints from the images directly, without the need to define them through manual engineering. The features obtained have a good degree of robustness to typical changes such as rotation, noise, and variability in the acquisition process. These features are further processed through an SVR model, unlike being stored in the form of fingerprint templates, thus preventing the risk of reconstruction attacks. Evaluation was performed on fingerprint images from the FVC2004 database, using image augmentation to simulate variability in pressure, orientation, and illumination conditions. The proposed framework achieved a recognition accuracy of 99.4%, an Equal Error Rate (EER) of 1.0%, and an Area Under the Curve (AUC) of 0.994, demonstrating strong robustness and reliability for privacy-preserving cancelable fingerprint recognition.
K. Gondi, Madhu Shukla· Engineering, Technology &...· 0 citations
A systematic framework is proposed, encompassing data acquisition, preprocessing, feature extraction, model training, and decision fusion, supported by mathematical formulations for classification, loss optimization, and evaluation metrics, offering a scalable approach for secure digital identity verification.
Salma El-Sayed· International Journal of App...· 0 citations
Multimodal biometric recognition integrates complementary information from different modalities and significantly improves recognition accuracy and system security compared with unimodal methods. However, with the widespread adoption of these systems, the risks of biometric template leakage and theft have emerged, posing serious threats to user privacy. To address this issue, we propose a secure multimodal template protection framework based on palmprint and palmvein. We design a feature-level fusion network, PalmSynNet, where the Multi-Scale Local Feature Extraction module enhances the extraction of local features, and the Global Feature Fusion module enables cross-modal global feature interaction, resulting in robust and highly discriminative fused representations. In addition, we propose a novel cancellable random projection method, which generates protected templates through SoftMax-based random projection (SoRP) combined with other hashing algorithms, effectively avoiding the reversibility problem of random projection under certain conditions. Extensive evaluations conducted on three multimodal palmprint databases—PolyU, TJ, and CUMT—demonstrate that proposed framework not only achieves superior fusion recognition performance compared with state-of-the-art methods, but also satisfies essential security requirements, including irreversibility, revocability, unlinkability, and resistance to various attacks.
Ce Gao, Jia-Qian Xu, Naiquan Wang et al.· IEEE Transactions on Informa...· 0 citations
Biometric authentication systems based on a single modality remain vulnerable to spoofing, acquisition noise, and intra-class variation, limiting their reliability in high-security access-control applications. This study presents an artificial neural network-based multimodal framework that combines facial recognition and fingerprint identification to improve authentication accuracy, robustness, and presentation-attack resistance. Facial features are extracted using a convolutional neural network, while fingerprint texture and minutiae representations are obtained using Gabor filters and a denoising autoencoder. Feature-level and score-level information is integrated through a multilayer-perceptron meta-learner, followed by an adaptive decision module incorporating modality-specific liveness assessment. The framework was evaluated using the Labeled Faces in the Wild dataset, FVC2006, and the custom Bayelsa Multimodal Biometric Dataset comprising 320 subjects. On the reported BMBD test split, the system achieved 99.14% verification accuracy, a false acceptance rate of 0.12%, a false rejection rate of 0.34%, and an equal error rate of 0.19%. The reported performance exceeded the best unimodal baseline by 6.8 percentage points and the strongest compared multimodal method by 1.93 percentage points. End-to-end inference required 143 ms on the stated embedded platform. The ablation results indicated that learned fusion, metric-learning losses, and liveness detection each contributed to performance. These findings support the feasibility of the proposed framework under the reported experimental conditions, while broader independent evaluation remains necessary.
Eric Omianwele, Daniel Ekpah· Asian journal of current res...· 0 citations
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