The study demonstrates the feasibility of deploying AI-powered facial age estimation as a complementary verification mechanism within electoral systems and provides a context-aware solution for strengthening voter eligibility verification and electoral credibility in emerging democracies.
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
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 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
EquiAI is proposed, a robust fairness-aware remote identity verification framework that integrates a pretrained Vision Foundation Model (DINOv2), fairness-aware representation learning, adaptive feature alignment, presentation attack detection, and explainable artificial intelligence into a unified architecture.
Suman Kumar Sanjeev Prasanna· Journal of Intelligent Decis...· 0 citations
Identity verification of voters is a very important process in the provision of fair and fraud free elections. In many polling stations, voter identity is still verified manually. Election officers compare the voter's face and ID card with the details available in the voter records before allowing them to vote. Although this method has been used for many years, it mainly depends on human observation, which can lead to mistakes. It also increases the possibility of impersonation and the use of fake identity documents. To overcome these issues, this paper presents a KYC-based voter verification system that uses computer vision and deep learning for automatic identity verification. The proposed system performs verification in two stages. First, a face recognition model compares the live image of the voter with the registered image stored in the database. Next, a YOLO-based object detection model, along with Optical Character Recognition (OCR), detects and reads the roll number from the voter's ID card. The extracted roll number is then matched with the database record. A voter is allowed to proceed only when both the face and the ID card details are successfully verified. By combining these two verification methods, the system helps reduce impersonation and provides a more secure and reliable voter verification process.
Nandana C K, Sarah Saju Muhammed, Manazhy Reshmi· 2026 International Conferenc...· 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
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