Mahesh Goyani

Masked Face Restoration Using GANs: A Survey on Recognition, Detection, and Inpainting

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.

Enhancing Continuous Sign Language Recognition through MGPT-based Segmentation and Structured Position-Aware Decoding

Continuous Sign Language Recognition (CSLR) has always stood quite difficult due to issues like coarticulation effects, availability of weak temporal annotations, and large variability among different signers, especially in a limited-resource scenario such as Indian Sign Language (ISL). Most of the current methods use end-to-end sequence modeling, which leaves out the explicit temporal structure and does not work well with weak supervision. Here, we propose a structured CSLR system that combines motion-guided segmentation, multimodal representation learning, and position-aware decoding aimed at overcoming these issues. More importantly, we present an MGPT-based temporal segmentation method that uses optical-flow-driven motion signals and Gaussian peak modeling to separate continuous signing sequences into consistent motion segments, which results in the reduction of transitional ambiguity. The spatial-temporal features are obtained with the help of a dual-stream architecture that integrates ResNet50-based visual representations and skeleton keypoint features, being then temporally modeled by a multi-layer LSTM network. To improve sequence-level consistency, we also introduce a Word Position Graph (WPG) for structured decoding along with Gaussian-weighted frame voting to highlight informative temporal regions and, at the same time, downplay noisy transitions. The approach we suggested was tested on the ISL-CSLRT dataset with weak sentence-level supervision. The experimental results show that our framework reaches 92% accuracy and a Word Error Rate (WER) of 0.07, greatly beating the baseline voting strategies. Statistical verifications, including multi-run evaluation and significance testing, have confirmed the robustness of the improvements. Also, comparing with representative CSLR methods has shown that the method of explicit temporal segmentation and position-aware decoding is very effective, especially when the dataset is scarce. Besides, the results indicate that introducing motion-consistent segmentation and structured decision fusion seems to be a good way for updating the CSLR systems beyond simply endwise paradigms.

Mitigating Adversarial Vulnerabilities in Deep Learning-Based Face Recognition Using Stacked Attention Residual GAN and Fire Hawk Optimization

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.

Infrared–visible image fusion for robust visual perception: methods, benchmarks, evaluation and open challenges