ABSTRACT
After the outbreak of novel coronavirus (COVID-19) in late December 2019, in an attempt to mitigate its development, the decision to close institutions around the world was made. To continue imparting education and delivering the learning material to their students, many institutions adopted for digital or E-learning. To support those institutions attempting to digitize their learning during this pandemic, the main aim of this study is to examine the students’ accessibility to and success of E-learning portals. Using the DeLone and McLean (D&M) Model, the study explains the differences between female and male students’ accessibility to E-learning portals. This study compares female and male student groups regarding the usage of the E-learning portal in the higher education context. Using an online google survey, the data were collected from 254 students, including males and females. The study utilized PLS-SEM to perform a multi-group analysis examining female and male student groups. The study found a significant and direct relationship of e-service quality with system use and user satisfaction for females and male student groups. System quality also supported the relationship with user satisfaction. The study further revealed a significant and positive relationship between system use and user satisfaction with E-learning portal success for females and male student groups. This study also concluded that insignificant difference exists in using the E-learning portal between female and male student in higher education institutions. Copyright © 2022 Shams, Niazi, Gul, Mei and Khan.
ABSTRACT
Due to the remarkable progress in recent years, deep face recognition is in great need of public support for practical model production and further exploration. The demands are in three folds, including 1) modular training scheme, 2) standard and automatic evaluation, and 3) groundwork of deployment. To meet these demands, we present a novel open-source project, named FaceX-Zoo, which is constructed with modular and scalable design, and oriented to the academic and industrial community of face-related analysis. FaceX-Zoo provides 1) the training module with various choices of backbone and supervisory head;2) the evaluation module that enables standard and automatic test on most popular benchmarks;3) the module of simple yet fully functional face SDK for the validation and primary application of end-to-end face recognition;4) the additional module that integrates a group of useful tools. Based on these easy-to-use modules, FaceX-Zoo can help the community to easily build stateof-the-art solutions for deep face recognition and, such like the newly-emerged challenge of masked face recognition caused by the worldwide COVID-19 pandemic. Besides, FaceX-Zoo can be easily upgraded and scaled up along with further exploration in face related fields. The source codes and models have been released and received over 900 stars at https://github.com/JDAI-CV/FaceX-Zoo. © 2021 ACM.
ABSTRACT
Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different modalities. Existing deep learning based methods have made remarkable progress in NIR-VIS face recognition, while it encounters certain newly-emerged difficulties during the pandemic of COVID-19, since people are supposed to wear facial masks to cut off the spread of the virus. We define this task as NIR-VIS masked face recognition, and find it problematic with the masked face in the NIR probe image. First, the lack of masked face data is a challenging issue for the network training. Second, most of the facial parts (cheeks, mouth, nose) are fully occluded by the mask, which leads to a large amount of loss of information. Third, the domain gap still exists in the remaining facial parts. In such scenario, the existing methods suffer from significant performance degradation caused by the above issues. In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method. Specifically, we propose a novel heterogeneous training method to maximize the mutual information shared by the face representation of two domains with the help of semi-siamese networks. In addition, a 3D face reconstruction based approach is employed to synthesize masked face from the existing NIR image. Resorting to these practices, our solution provides the domain-invariant face representation which is also robust to the mask occlusion. Extensive experiments on three NIR-VIS face datasets demonstrate the effectiveness and cross-dataset-generalization capacity of our method. IEEE