In the rising era of globalization and digitization , remote education continues in gaining popularity and reach. Efficiently proctoring online remote examination is an important limiting factor to sustain the integrity of the exam as well as provide unprejudiced results. Currently human proctoring is the customer perspective to maintain integrity, either manually with the help of a test taker or by overseeing them visually through webcams. Online exams provide the examiner the choice, to choose the environment and the tools they wish to use during the exam. In response to this, our research proposes an application to detect fraudulent activities during online examination in real-time through the video recorded by the webcam of the examiner’s system. The application provides four features that continuously estimate the integrity of the exam: (1) User verification for checking impersonation by the examiner. (2) Multiple people together solving the exam. (3) Absence of examiner. (4) Detecting the use of mobile phones. The extensive experiment depicts accuracy of our cost-effective remote proctoring.
For a wide range of systems that requires reliable personal recognition schemes to either determine or confirm the identity of a person requesting the services, personal recognition using palm-vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. Palm vein imaging requires near infrared (NIR) light for the complex vascular structures residing inside the palm to become visible. The blood vessels which absorb the NIR illumination appear darker than other tissues. In this paper analysis of various palm vein recognition techniques that are widely applied in today's palm vein recognition technology is represented. The process of the palm vein recognition starts right just from the process of image acquisition. In section III the pre-processing of the images is discussed which is followed by the feature extraction of the pre-processed image and the pattern recognition. After this a final result is obtained.
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