Abstract: This paper presents a review of research in video face recognition conducted over the past five years. It provides a comparative analysis of the facial feature computation methods, categorizing them into traditional approaches, deep learning techniques, and feature aggregation/fusion methods. Traditional feature extraction methods include linear and nonlinear approaches, while deep learning methods include spatial and temporal feature extraction techniques. Feature aggregation and fusion methods integrate multiple feature sources and fuse features from different time periods to improve recognition performance. At the end of each subsection, this paper also provides a unified analysis of the algorithms used in the literature, highlighting their advantages, evaluation metrics, and applications. Through this research, we aim to provide more reliable and efficient solutions for practical applications of video face recognition systems and promote further advancements in this field.

Key words: video face recognition, feature extraction, feature aggregation, feature fusion, deep learning