A hybrid framework for mask-resilient face recognition and anti-spoofing to enhance secure voter authentication

Enhancing Biometric Security Using Artificial Neural Network-Based Multimodal Fusion of Facial Recognition and Fingerprint Identification

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.

Robust User Authentication Using CNN-Based Face, Fingerprint, and Iris Biometrics

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.

Intelligent multimodal face anti-spoofing detection system based on biometrics

A multi-dimensional feature fusion-based face anti-spoofing detection method based on the YOLOv8 architecture that integrates dynamic optical flow features with static texture analysis and achieves robust detection through the fusion of spatial and temporal cues.

Comparative Analysis of Various Biometric Recognition along with Secure Multi-modal System

The study’s objective is to create a facial recognition system that is both lightweight and effective for secure biometric identification using MobileNetV3, and to assess how well it performs in comparison to more conventional, computationally demanding models. Group 2 (Intervention) refers to a MobileNetV3-based face recognition system with 30 samples and 98% confidence, while Group 1(control)relates to Conventional CNN based facial recognition models with 30 samples but low accuracy To guarantee high-quality inputs, data preprocessing methods like image alignment and normalization are employed. The model’s judgment are interpreted using SHAP (Shapely Additive Explanations), which also detects biases and identifies real inputs from fake attempts, such as images and movies, liveness detection techniques are used. When compared to conventional models, the MobileNetV3-based system achieved a far higher accuracy of 95% in facial recognition tasks. The system showed a Multi-Modal with 98% accuracy and low latency, which makes it appropriate for real-time applications. Transparency was greatly increased by SHAP-driven insights, which offered concise justifications for model choices. This study shows that the lightweight MobileNetV3-based facial recognition system outperforms conventional models in terms of accuracy, security, and explainability.

Toward High Accuracy and Strong Security: Cancellable Templates for Multimodal Biometric Recognition Based on Feature Fusion

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.

A Secure Multimodal Biometric Authentication Framework Using Watermarking and Feature-Level Fusion for Cyber-Physical System