Aug 2026· Engineering, Technology & Applied Science Research· Vol 16, pp. 37869-37874· 0 citations· 23 references
TL;DR
An occlusion-aware hybrid biometric framework for reliable 3D face recognition that reaches an accuracy of up to 98.7%, even in partial occlusions, and significantly reduces the Equal Error Rate, demonstrating its effectiveness and suitability for real-world biometric authentication applications.
Abstract
Face recognition in real-world and uncontrolled environments is greatly affected when the face is partially covered by masks, glasses, scarves, hair, or due to pose-related self-occlusion. Although three-dimensional (3D) face recognition is generally more robust to lighting changes and moderate pose variations, its performance still reduces when important facial regions are heavily covered. To overcome this problem, this study presents an occlusion-aware hybrid biometric framework for reliable 3D face recognition. The proposed method combines reconstructed 3D shape features, deep texture features, and additional biometric cues using an adaptive weighted fusion approach. An occlusion detection and generative reconstruction module is used to recover missing facial regions before feature extraction, and an attention mechanism reduces the impact of unreliable areas while focusing on important facial features. Extensive experiments on benchmark datasets, such as Bosphorus, BU-3DFE, and FRGC v2.0, show that the proposed framework performs better than strong single-modality and traditional multimodal methods. The proposed system reaches an accuracy of up to 98.7%, even in partial occlusions, and significantly reduces the Equal Error Rate (EER), demonstrating its effectiveness and suitability for real-world biometric authentication applications.
Face recognition with occlusion remains a challenging issue for biometric authentication systems in real-world scenarios. Recent Generative Adversarial Network (GAN)- based approaches have improved facial reconstruction under partial occlusion; however, recognition accuracy remains severely limited in regions with extensive facial occlusion. To address this limitation, this study proposes a multimodal biometric framework, Complete Face Recovery (CFR)-GAN++. The framework combines self-supervised 3D face reconstruction with physiological biometrics from Electroencephalography (EEG) and Electrocardiography (ECG). The proposed framework consists of a facial reconstruction generator based on U-Net, a CNN–BiLSTM EEG encoder, and a 1D CNN ECG encoder in an adaptive feature-level fusion framework. The visual stream reconstructs occlusion-corrupted facial regions with a self-supervision strategy of Swap-Rotate-and-Render and 3D Morphable Model (3DMM) regression.
M. L. Gangadhar, A. S. Raju, C. R. Roopashree· Engineering, Technology &...· 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
: Face recognition (FR) is a popular technology in the field of artificial intelligence and is a biometric technology based on facial features. However, in actual situations, images often have some uncontrollable factors, which can lead to a decline in image quality. Therefore, this paper mainly explores the application and performance of FR in complex scenarios such as occlusion, multi-person scenes, and uncontrolled environments. Among them, FR under occlusion discusses two types of recognition methods: occlusion aware face recognition (OAFR) and occlusion recovery based face recognition (ORecFR); FR in multi-person scenarios discusses the recognition of a single target object and group-level emotion recognition (GER); FR in uncontrolled environments discusses recognition under different lighting and makeup conditions. It aims to solve the problem of poor performance of FR in complex scenarios. Finally, the challenges faced by FR in this field and its prospects for the future were pointed out.
Kehao Zhou· Proceedings of the 3rd Inter...· 0 citations
Face recognition systems applied to smart surveillance settings often experience poor performance when the faces are partially occluded by a mask or other objects. Occlusions eliminate critical facial information, which makes face identification much more difficult for traditional deep learning models. To solve this issue, a hybrid deep learning model utilizing convolutional neural networks and transformer-based attention mechanism is proposed in this study for robust masked and occluded face recognition. The framework uses the ResNet50 backbone for obtaining the discriminative local facial favorable features, and the Vision Transformer module for obtaining long-range context relationships between facial regions. In addition, an Adaptive Occlusion Attention Module is introduced to Zurcrook visible facial areas and neglect the corrupted features to occlusions. Experiments were carried out on the Real-World Masked Face Dataset (RMFD) with 1205 images of 25 identities. The proposed model attained 93.46% training accuracy and Top-1 and Top-5 recognition accuracy were 51.87% and 81.33%, respectively. Additional occlusion experiments resulted in occlusion recognition accuracy of 28.63% and cross-dataset evaluation using MaskedFace-Net resulted in an average feature similarity of 0.8288. The results show that the proposed hybrid architecture enhances the recognition robustness of masked and partially obstructed facial images facing the surveillance situation.
R. R, Anbalagan E· 2026 4th International Confe...· 0 citations
Emergence of masked face recognition (MFR) as a pivotal area in biometric identification has been significantly accelerated by the global COVID-19 pandemic. In response, the research community has developed a variety of innovative techniques to address recognition and detection under occlusion, with a growing emphasis on Generative Adversarial Networks (GANs) for masked face restoration and inpainting. We examined three interconnected sub-domains: Masked Face Recognition (MFR), Face Mask Detection, and Face Unmasking (FU), each addressing unique aspects of the problem from identifying individuals with partially or fully covered faces to reconstructing occluded facial regions for improved accuracy. The core focus of this paper is on the role of GANs in overcoming occlusion by synthesizing realistic facial textures in the masked regions, thereby restoring the identity cues. Beyond technical developments, the paper analyzes the limitations and open research problems, such as maintaining identity consistency in restored images, handling diverse mask types and occlusion levels, and ensuring generalizability across different demographic groups and environments. By integrating insights from recent advances and identifying existing research gaps, this survey aims to serve as a comprehensive reference for academics and practitioners engaged in the development of robust, privacy-aware, and ethically responsible masked face recognition systems enhanced by GANs.
Payal Parekh, Hina Choksi, Mahesh Goyani et al.· ITEGAM- Journal of Engineeri...· 0 citations
Facial occlusion degrades face recognition by creating scale-inconsistent identity cues across visible regions and amplifying responses to irrelevant occluders. To address these coupled problems, this study proposes a pyramid-guided multi-scale attention framework based on scale alignment and reliability-aware feature refinement. Hierarchical features are first projected into a shared semantic space, after which local, intermediate, and global dependencies are adaptively weighted according to the available facial information. Channel–spatial refinement is then used to suppress unreliable responses from occluded regions, while an angular-margin objective preserves inter-identity separability from incomplete facial evidence. Experiments on CASIA-WebFace and occluded LFW show that the proposed method achieves an accuracy of 99.26% under clean conditions and 82.72% under occlusion, with an ROC-AUC of 0.8351. At 80% occlusion, the proposed method outperformed the strongest recent baseline, HMPA-GFAF, by 1.41% points and the direct Inception-ResNet-v1 + ArcFace baseline by 5.88% point. These results demonstrate that the proposed framework improves occlusion robustness without sacrificing clean-face recognition performance, indicating its practical potential for identity verification and access-control applications involving masks, glasses, and other partial facial occlusions.
Unknown authors· Discover Computing· 0 citations
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