  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.
