Device-Edge-Cloud Collaborative Acceleration Method Towards Occluded Face Recognition in High-Traffic Areas

Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16%while retaining the equivalent recognition accuracy. IEEE

An approach to unveil the masked faces: Occluded Face-Recognition System

Face recognition is one of the most widely used biometric identification systems due to its practicality and ease of use. The COVID-19 outbreak has recently expanded rapidly over the world, posing a major threat to people's health and economic well-being. Using masks in public places is an efficient approach to prevent the spread of infections. However, due to the absence of facial features detail, a masked face recognition system is a difficult task. We offer a technique to identify the masked faces in this project.Masked face recognition is a subset of occluded face recognition that requires prior knowledge of the obscured portion of the targeted face. Occluded face recognition is a current research topic that has captured the interest of the computer vision community. Occluded face recognition systems have previously been focused on detecting and recognizing an individual's face in the wild when the occluded part of the face is in a random form and position. Meanwhile, the nose, mouth, and cheeks of a masked face are frequently hidden. The eyes, brows, and forehead may be the only remaining clear areas. As a result, a masked face recognition system could effectively concentrate on analyzing traits that can be derived from the subject's uncovered portions, such as the eyes, brows, and forehead. © 2022 IEEE.

Recognition of Faces Wearing Masks Using Skip Connection Based Dense Units Augmented With Self Restrained Triplet Loss

Facial recognition-based systems are the most efficient and cost-effective of all the contactless biometric verification systems available. But, in the COVID-19 scenario, the performance of available facial recognition systems has been affected badly due to the presence of masks on people's faces. Various studies have reported the degradation of the performance of facial recognition systems due to masks. Therefore, there is a need for improvement in the performance of currently available facial recognition algorithms. In this research, we propose using Skip Connection based Dense Unit (SCDU) trained with Self Restrained Triplet Loss, to handle the embeddings produced by existing facial recognition algorithms for masked images. The SCDU is trained to make facial embeddings for unmasked and masked images of the same identity similar, as well as, embeddings for unmasked and masked images of different identities dissimilar. We have evaluated our results on the LFW dataset with synthetic masks as well as the real-world masked face recognition dataset, i.e., MFR2 and achieved improvement in verification performance in terms of Equal Error Rate, False Match Rate, False Non-Match Rate, and Fisher discriminant ratio. © 2022 IEEE.

Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition

Over the years, the evolution of face recognition (FR) algorithms has been steep and accelerated by a myriad of factors. Motivated by the unexpected elements found in real-world scenarios, researchers have investigated and developed a number of methods for occluded face recognition (OFR). However, due to the SarS-Cov2 pandemic, masked face recognition (MFR) research branched from OFR and became a hot and urgent research challenge. Due to time and data constraints, these models followed different and novel approaches to handle lower face occlusions, i.e., face masks. Hence, this study aims to evaluate the different approaches followed for both MFR and OFR, find linked details about the two conceptually similar research directions and understand future directions for both topics. For this analysis, several occluded and face recognition algorithms from the literature are studied. First, they are evaluated in the task that they were trained on, but also on the other. These methods were picked accordingly to the novelty of their approach, proven state-of-the-art results, and publicly available source code. We present quantitative results on 4 occluded and 5 masked FR datasets, and a qualitative analysis of several MFR and OFR models on the Occ-LFW dataset. The analysis presented, sustain the interoperable deployability of MFR methods on OFR datasets, when the occlusions are of a reasonable size. Thus, solutions proposed for MFR can be effectively deployed for general OFR. © 2022 IEEE.

Identifying People Wearing Masks in a 3D-Scene

Now people are facing the pandemic COVID-19 and have to wear masks. This brings a problem in face recognition—occlusion problem and particularly, identifying people wearing masks in 3D-scenes is a great challenge. This study aims to develop a system for tackling this challenge. The 3D-scene is constructed with the 2D-3D coordinate transformation. For the convenience of the fusion between the virtual scene and real scene, a 3D model is achieved by Sketchup Pro. The faces and masks data are explored from the video and occluded faces recognition is achieved with the convolutional neural network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.