## 1 Introduction  
With the continuous progress of artificial intelligence technology, different fields of education have been deeply affected. In this context, different countries have formulated relevant policies aimed at promoting the deep integration of intelligent technology and education, empowering teaching with intelligent technology to innovate education systems and improving education quality. In May 2023, the Office of Educational Technology of the United States released a report titled \"Artificial Intelligence and the Future of Teaching and Learning\" [1] which presents the latest insights and policy recommendations on the development of artificial intelligence in teaching. In January 2024, the Chinese Ministry of Education proposed at the World Digital Education Conference that it would implement artificial intelligence empowerment actions to promote the deep integration of intelligent technology and education [2]. As an important part of ensuring educational fairness, invigilation puts forward the new requirements for its intelligent development. Therefore, the improvement of traditional invigilation forms has become an urgent problem to be solved. With the support of intelligent technology, achieving the intelligent auxiliary monitoring of cheating behavior in examinations can greatly reduce the burden on invigilators, improve the efficiency of invigilation, and maintain the fairness and impartiality of the examination. Cheating can be accomplished individually or with the assistance of others. Individual cheating refers to the independent use of cheating tools by candidates during an exam,and cheating with the assistance of others refers to the indirect use of cheating tools with the help of third parties. Due to the concealment, complexity, and diversity of cheating behaviors, the development of automatic online examination monitoring faces challenges. To address these issues, methods have been proposed to detect cheating behaviors based on visual, acoustic, physiological, and other signals. Among these, the use of cameras for remote monitoring is the most effective and intuitive method. According to the location and number of cameras, there are both single- and dual-perspective cheating detection methods. The common single-perspective method uses the candidate's face as the monitoring viewpoint [3] and determines cheating behavior by identifying the gaze direction [4; 5] or face direction [6; 7]. This method has the advantages of fewer sensors, lower cost, and faster processing; however, there is the problem of insufficient evidence of cheating. Dual-perspective methods monitor from two directions simultaneously, such as the face and overhead perspective [8] or the face and horizontal perspective [9; 10]. Dual-perspective methods have a wider monitoring range and provide more comprehensive evidence of cheating than single-perspective methods. However, they still have shortcomings. Specifically, the combination of the face and overhead lacks the perspective of the candidate's field of view. When the candidate looks up at the cheating tool in the field of view, the two cameras cannot detect the cheating tool due to a large monitoring blind zone, as shown in Figure 1. The range of view of the glasses camera differs greatly from the actual gaze direction of the candidate. Especially when the candidate looks down at the desktop, the close distance between the glasses and desktop can cause a narrow range of view, resulting in a blind zone, as shown in Figure 1.  
In response to the above issues, this paper proposes a multi-perspective adaptive paperless examination cheating detection system. At present, active window detection algorithms have often been embedded in paperless examination systems to monitor whether students engage in cheating by using a hidden second screen on the computer screen. Therefore, this study aims to assist in the invigilation of paperless examination systems from a visual monitoring perspective.