A Video-Based Cognitive Emotion Recognition Method Using an Active Learning Algorithm Based on Complexity and Uncertainty

The cognitive emotions of individuals during tasks largely determine the success or failure of tasks in various fields such as the military, medical, industrial fields, etc. Facial video data can carry more emotional information than static images because emotional expression is a temporal process. Video-based Facial Expression Recognition (FER) has received increasing attention from the relevant scholars in recent years. However, due to the high cost of marking and training video samples, feature extraction is inefficient and ineffective, which leads to a low accuracy and poor real-time performance. In this paper, a cognitive emotion recognition method based on video data is proposed, in which 49 emotion description points were initially defined, and the spatial–temporal features of cognitive emotions were extracted from the video data through a feature extraction method that combines geodesic distances and sample entropy. Then, an active learning algorithm based on complexity and uncertainty was proposed to automatically select the most valuable samples, thereby reducing the cost of sample labeling and model training. Finally, the effectiveness, superiority, and real-time performance of the proposed method were verified utilizing the MMI Facial Expression Database and some real-time-collected data. Through comparisons and testing, the proposed method showed satisfactory real-time performance and a higher accuracy, which can effectively support the development of a real-time monitoring system for cognitive emotions.