Jul 2026· JOIV: International Journal on Informatics Visualization· Vol 10, pp. 1731· 0 citations
TL;DR
A novel deep multimodal biometrics framework called ForensicNet is presented, which combines heterogeneous biometric modalities: face images, speech signals, and fingerprint patterns into a combined expression of robust forensic identity attribution.
Abstract
Biometric identification is a vital feature in digital forensics, and unimodal systems of biometric identification, like face, voice, or fingerprint recognition, tend to experience poor performance under a contaminated situation or when complete. In this paper, a novel deep multimodal biometrics framework called ForensicNet is presented, which combines heterogeneous biometric modalities: face images, speech signals, and fingerprint patterns into a combined expression of robust forensic identity attribution. The architecture will be defined based on joint representation learning, deep fusion with an attention-based weighting layer, and an adaptive reliability module that recalibrates decisions when one or more biometric sources are partially unavailable. For evaluation, extensive experiments were performed on the three standard data sets: forensic face recognition, speaker verification, and fingerprint matching. The results suggest that ForensicNet achieves 19.3 percent higher identification accuracy than the unimodal baseline and conventional fusion baseline, a 22.7 percent lower equal error rate (EER), and 16.8 percent better robustness to missing modalities. Furthermore, ForensicNet can obtain these benefits while using 13.1 fewer computational overheads. Overall, the above results have proven ForensicNet to be a promising, scalable, reliable, and energy-saving forensic tool for practical law enforcement, border security, and digital identity verification solutions.
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
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
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
A survey of demographic attribute estimation from periocular images, covering publicly available datasets, methodological trends from handcrafted descriptors to deep learning architectures, and the state of the art in gender, age, and ethnicity prediction is provided.
F. Alonso-Fernandez, Kevin Hernandez-Diaz, J. Bigun· 0 citations
Biometric recognition systems have become an indispensable piece of contemporary security and recognition administration frameworks. Multimodal biometric systems incorporate multiple biometric features for verification provide significant advantages over uni-modal biometric systems, to name a few being, improved security, enhanced accuracy and considerable amount of robustness. Across a sample of individual modalities, face recognition is extensively applied for its reliability and ease of use, voice recognition is eminent for its high accuracy and stability. This study highlights the significance of multimodal biometric authentication method using face images and voice signals called, Multi Task Cascaded Pre-emphasis and Vision Attention Fusion Transformer (MTCP-VAFT) to enhance the security of existing multimodal biometric recognition systems in indoor surveillance videos. It introduces a novel pre-processing model that simultaneously processes face images using Multi Task Cascaded Neural model and voice signals utilizing and Pre-emphasis Filter. This work also proposes a novel Vision Attention Fusion Transformer based multimodal biometric authentication to exploit their complementary advantages and mitigate attacks. Finally the method incorporates the two modalities via an attention fusion mechanism to highlight the multimodal biometric recognition system reliability under varying circumstances. Experimental results on the MSU-AVIS dataset demonstrate the efficiency of our method, showing notable improvements achieving overall multimodal biometric recognition accuracy by 97% and improving the peak signal to noise ratio by 48.25dB. These findings demonstrate that modality-aware fusion using MTCP-VAFT method can delivery secure and flexible biometric authentication suitable for deployment on high-security platforms.
S. Preethi, P. J. Charles· THE SCIENTIFIC TEMPER· 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
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