Person-based indices and timelines can enable fast and non-linear access to recorded meetings. This paper focuses on how to automatically construct those indices and timelines by using face recognition techniques. While there exist extensive research in generic face recognition, recognizing faces in recorded meetings is still an understudied area. Real-world meeting videos impose several interesting and unique challenges including complex lighting, low imaging quality, and large variations in head pose and size. In this paper, a promising approach based on MRC-Boosting is presented to address these challenges, which achieves encouraging performance on real-world meeting videos and shows superior accuracy and robustness compared to two popular existing approaches.