Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Face recognition becomes an important biometric implementation in any surveillance, access control, forensic and intelligent security system applications. Despite of technical advances in deep learning models, recognition accuracy normally affected by facial pose, illumination, occlusion, expression, aging factors and limited availability of labelled dataset. These challenges reduce the robustness of face recognition models in real world environments [1][2][3]. Recent researches demonstrated that the super-resolution techniques based on Generative Adversarial Networks (GANs) reconstruct high-quality facial images very effectively from low-resolution input images. It supports to improve feature representation and face recognition [4][5]. Therefore, this research motivated from this advancement and proposes an Adaptive Super-Resolution Generative Adversarial Network (Adaptive SRGAN) for face recognition. It 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. However, it enhances reconstruction quality by preserving discriminative facial characteristics [6]. The proposed model is expected to achieve higher Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), recognition accuracy, precision, recall, and F1-score while reducing false acceptance and false rejection rates. Simultaneously, Adaptive SRGAN delivers a robust and scalable solution for improving face recognition systems.
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
A Self supervised Capsule Network (SS-CapsNet) integrates convolutional feature extraction, capsule based learning, dynamic routing and contrastive self supervised learning into a combined framework for robust face recognition.
K. Minney Prisilla, N. Jayashri· International journal of com...· 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
The rapid advancement of digital technology has transformed attendance systems in educational environments. Conventional manual attendance methods are vulnerable to manipulation, while fingerprint-based biometric systems often experience limitations due to certain physical conditions. Facial recognition has therefore emerged as a reliable and non-contact biometric solution for attendance verification. However, existing deep learning architectures such as the Vision Transformer (ViT) require substantial computational resources and memory during training. This study proposes the Vision Reformer (ViR) model to enhance the computational efficiency of the ViT architecture for facial recognition. The proposed approach integrates the memory-efficient attention mechanism of the Reformer architecture into the ViT framework to improve training efficiency while maintaining high recognition performance. Experiments were conducted using the Indonesian Muslim Student Face Dataset (IMSFD) with learning rates of and . The experimental results show that the ViR model with a learning rate of and achieved the best performance, with accuracy of 94.63%, F1-score of 94.77%, precision of 95.89%, and recall of 94.63%, while requiring only 41.076 seconds of training time. In comparison, the ViT model achieved accuracy of 94.11% and required 82.368 seconds under the same configuration. Additionally, GPU memory analysis indicates that ViR reduces memory consumption by 78 MB and 39 MB at batch sizes of 16 and 32, respectively, while maintaining a stable and linear memory usage pattern. These results demonstrate that the proposed Vision Reformer model improves computational efficiency and memory utilization while maintaining competitive accuracy, making it a practical solution for facial recognition-based attendance systems in educational settings.
Muhammad Dedi Saputra, Khairun Saddami, N. Nasaruddin· Jurnal Nasional Teknik Elekt...· 0 citations
Optimize Deep Learning–based Adversarial Defense Mechanism (ODL-ADM) is proposed in this work, which projects adversarial samples into an immune feature space that is both discriminative and resistant to perturbations.
Sheilla Ann Bangoy Pacheco, Mahesh Goyani, Jayzel P. Bangoy et al.· ITEGAM- Journal of Engineeri...· 0 citations
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