Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 312-319· 0 citations
TL;DR
The proposed model combines deep learning-based feature extraction with machine learning classifiers to detect face morphing attacks and strengthen biometric authentication systems applicable to border control, ID verification, and digital authentication.
Abstract
Facial recognition systems are widely used for identity verification but are vulnerable to face morphing attacks, where
multiple facial images are blended to form a deceptive identity that can fool recognition models. This project develops a deep
learning-based approach to detect such attacks and strengthen biometric authentication systems.
It lies in the domain of Artificial Intelligence and Machine Learning, focusing on Computer Vision techniques to differentiate
real and morphed facial images for accurate and secure verification.
The project involves creating realistic morphed face datasets and building an efficient detection model applicable to border
control, ID verification, and digital authentication.
Current systems fail against high-quality morphs produced using advanced tools, showing reduced accuracy under variations in
lighting, age, and facial accessories.
To overcome this, the proposed model combines deep learning-based feature extraction with machine learning classifiers.
Morph-2 and Morph-3 datasets are generated using professional morphing tools, and image enhancement with feature fusion is
applied to improve accuracy and robustness.
The findings suggest that the proposed framework provides an efficient, integrated solution for enhancing security in electoral systems and can be extended to other security-critical domains and offers strong potential for real-world biometric authentication applications.
Opeyemi Lateef Usman, Khadijah Opeyemi Owodunni· The Scientific World Journal· 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
The proposed Adaptive Super-Resolution Generative Adversarial Network (Adaptive SRGAN) integrates adaptive learning with image super resolution to reconstruct identity preserving high resolution facial images by employing adaptive learning rate optimization, dynamic loss weighting, attention guided feature enhancement and identity preserving loss functions.
M. Kirubakaran, A. S. Aneeshkumar· International journal of com...· 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
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.
S.Hamsanandhini, K.S.Manojee, P.Palanisamy et al.· 2026 4th International Confe...· 0 citations
A comparative analysis of existing studies is presented to highlight the evolution of deep learning techniques and their effectiveness in improving recognition accuracy and computational efficiency and emerging research directions are outlined to provide insights for future research.
Patel Bhautika Ronak· International journal of res...· 0 citations
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