Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio

On using gait to enhance frontal face extraction

Visual surveillance finds increasing deployment formonitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose. In surveillance environments, it is necessary to handle pose variation of the human head, low frame rate, and low resolution input images. We describe the first use of gait to enable face acquisition and recognition, by analysis of 3-D head motion and gait trajectory, with super-resolution analysis. We use region- and distance-based refinement of head pose estimation. We develop a direct mapping to relate the 2-D image with a 3-D model. In gait trajectory analysis, we model the looming effect so as to obtain the correct face region. Based on head position and the gait trajectory, we can reconstruct high-quality frontal face images which are demonstrated to be suitable for face recognition. The contributions of this research include the construction of a 3-D model for pose estimation from planar imagery and the first use of gait information to enhance the face extraction process allowing for deployment in surveillance scenario