2021· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
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.
Abstract
Digital identity verification has become essential across online banking, e-governance, healthcare, education, and e-commerce. While biometric systems such as facial recognition, fingerprint scanning, and iris detection enhance convenience and scalability, they remain vulnerable to spoofing attacks including photo/video replays, deepfakes, silicone masks, and display-based presentation attacks. Liveness detection has therefore emerged as a critical security layer to distinguish genuine biometric traits from fraudulent representations. This paper presents a comprehensive study of liveness detection models, covering architecture, algorithms, and performance evaluation. It examines active and passive techniques, including texture analysis, motion cues, physiological signal extraction, and deep learning approaches. Traditional handcrafted features are compared with CNNs, RNNs, and transformer-based models. Emphasis is placed on multimodal biometrics and challenge-response mechanisms to counter advanced GAN-based deepfake attacks. 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. Experimental results on benchmark datasets demonstrate improvements in accuracy, FAR, and APCER, with hybrid deep learning models integrating temporal and physiological cues outperforming single-modality methods. The paper concludes by addressing deployment challenges, ethical considerations, and future directions such as privacy-preserving learning, federated identity systems, and explainable AI security, offering a scalable approach for secure digital identity verification.
With the explosive growth of generative AI technologies, the threat posed by spoofing attacks and deepfake attacks on biometric authentication systems continues to grow. Traditional biometric systems rely on visual presentation and are subject to various types of presentation attacks such as replay attacks, printed photographs, and synthetic deepfake identities. This paper presents an Adaptive Multimodal Liveness Detection Framework (AMLF) to increase the strength of biometric identity systems and reduce their vulnerability to both spoofing and deepfake attacks. The framework also aims to reduce the computational costs of performing liveness detection on resource-constrained edge devices. The proposed framework uses three techniques for liveness detection across multiple biometric modalities (face, fingerprint, and iris): spatial texture analysis, temporal biometric signals, and frequency-domain artifacts. A lightweight, deep neural network architecture is used for the multimodal liveness detection process on edge devices and enables real-time liveness detection. A series of experiments conducted on publicly available datasets (FaceForensics++ , CASIA-Iris-V4, MSU-MFSD, and FVC2006) demonstrate that the proposed AMLF considerably increases the accuracy of detecting spoofing attacks over existing approaches based on deep learning methods while significantly reducing the computational costs associated with these methods. Overall, the AMLF framework provides a highly effective means to improve the capability of next generation biometric authentication systems, particularly in an edge computing environment.
Ankita Kotalwar, R. Joshi· Discover Artificial Intellig...· 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 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.
Yanhua Liang, Pengcheng Zhou, Hongmei Qin et al.· International Conference on...· 0 citations
Automatic signature verification is a complex biometric authentication problem because signatures involve intent, distinct writing styles, visual texture, and motor dynamics. This review consolidates signature verification work involving offline, online, mobile, contactless, and in-air verification. The reviewed works show a clear shift from traditional classifiers and handcrafted descriptors toward deep metric learning, convolutional feature extractors, capsule models, and recurrent temporal models. Stronger image-level representation does not mean deployment-ready security. There are still many modern cyberattacks and challenges related to skilled forgery, replay attacks, presentation attacks, sensor variability, demographic and cross-linguistic transfer, data scarcity, and privacy-preserving learning. Thus, the paper analyzes those challenges and presents an original, practical taxonomy for over 50 selected works and standards. It also outlines available datasets and measures of performance and suggests a set of best-case solutions in the context of a given operational constraint, an expanded wearable-technology analysis, and a trustworthiness framework for explainable and auditable biometric decision-making. The future works for secure biometric services are the integration of advanced computer vision, calibrated and reliable security thresholds, a range of methods for detection of presentation attacks, assurance of cancellability, continuous post-deployment assessment, and privacy are some key components.
Received: 24 May 2026 | Revised: 23 July 2026 | Accepted: 11 August 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Author Contribution Statement
Amal Hameed Khaleel: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Suhad Muhajer Kareem: Conceptualization, Methodology, Validation, Resources, Writing – review & editing.
A. Khaleel, S. M. Kareem· Smart Wearable Technology· 0 citations
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.
Shaik Badulla, Vundavalli Balasankar, G. L. V. Prasad· 2026 4th 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
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