Facial Recognition in Collaborative Learning Videos

TL;DR

This paper presents a fast and accurate face recognition system tailored for collaborative learning videos, effectively handling occlusions, pose variations, and long durations by leveraging past detection data and prototype face collections.

Contribution

The authors introduce a dynamic face recognition system that improves speed and accuracy in challenging collaborative learning video scenarios, outperforming baseline methods.

Findings

Achieved 86.2% accuracy, surpassing baseline's 70.8%.

Recognition was 28.1 times faster than the baseline.

Effectively handled occlusion and pose variation challenges.

Abstract

Face recognition in collaborative learning videos presents many challenges. In collaborative learning videos, students sit around a typical table at different positions to the recording camera, come and go, move around, get partially or fully occluded. Furthermore, the videos tend to be very long, requiring the development of fast and accurate methods. We develop a dynamic system of recognizing participants in collaborative learning systems. We address occlusion and recognition failures by using past information about the face detection history. We address the need for detecting faces from different poses and the need for speed by associating each participant with a collection of prototype faces computed through sampling or K-means clustering. Our results show that the proposed system is proven to be very fast and accurate. We also compare our system against a baseline system that uses InsightFace [2] and the original training video segments. We achieved an average accuracy of 86.2% compared to 70.8% for the baseline system. On average, our recognition rate was 28.1 times faster than the baseline system.