Fast Video-based Face Recognition in Collaborative Learning Environments

Face recognition is a classical problem in Computer Vision that has experienced significant progress. Yet, in digital videos, face recognition is complicated by occlusion, pose and lighting variations, and persons entering/leaving the scene. The thesis's goal is to develop a fast method for face recognition in digital videos that is applicable to large datasets. The thesis introduces several methods to address the problems associated with video face recognition. First, to address issues associated with pose and lighting variations, a collection of face prototypes is associated with each student. Second, to speed up the process, sampling, K-means Clustering, and a combination of both are used to reduce the number of face prototypes per student. Third, the videos are processed at different frame rates. Fourth, the thesis proposes the use of active sets to address occlusion and to eliminate face recognition application on video frames with slow face motions. Fifth, the thesis develops a group face detector that recognizes students within a collaborative learning group, while rejecting out-of-group face detections. Sixth, the thesis introduces a face DeID for protecting the students' identities. Seventh, the thesis uses data augmentation to increase the training set's size. The different methods are combined using multi-objective optimization to guarantee that the full method remains fast without sacrificing accuracy. To test the approach, the thesis develops the AOLME dataset of 138 student faces (81 boys and 57 girls) of ages 10 to 14, who are predominantly Latina/o students. Compared to the baseline method, the final optimized method resulted in fast recognition times with significant improvements in face recognition accuracy. Using face prototype sampling only, the proposed method achieved an accuracy of 71.8% compared to 62.3% for the baseline system, while running 11.6 times faster.