## 1 Introduction  
In the era of Industry 4.0, humans play an important role in the design, installation, control, updating, and maintenance of intelligent systems [1]. With the rapid improvements in system performance, the increase in system complexity has demanded higher requirements for the skills and reliability of system operators during tasks [2]. On the other hand, increasingly mature Cyber Physical systems [3] based on intelligent computing seem to be pushing the use of automated and unmanned industrial and military tasks, but the actual operators of complex systems such as intelligent transportation systems, industrial control systems, and unmanned aerial vehicles (UAVs) are still human beings. The ever-changingscenes and massive data emerging from Cyber Physical systems will force humans to face more urgent and complex task scenarios to some extent. In this scenario, human-centered system reliability and security are even more important. Human errors are indeed becoming the main cause of accidents nowadays [4]. In particular, in the fields of nuclear power, aviation, aerospace, and the petrochemical industry, more than 60% of accidents and incidents are directly or indirectly caused by human error [5]. Therefore, Human Reliability Analyses (HRAs), which aim to analyze, predict, reduce, and prevent human errors [6], need to be given more attention in system design. Humans have always hoped to establish a cognitive and intelligent system that can distinguish and understand people's cognitive state and emotional expression, while making sensitive and friendly responses in a timely manner [7]. Understanding human cognition has become a focus, as people have found that human cognition is a key factor in determining human reliability. Some methods have emerged to study human reliability from a cognitive perspective, such as the Human Cognitive Reliability (HCR) [8] method and Cognitive Reliability and Error Analysis Method (CREAM) [9]. Cognition and emotion are two important parts of human psychological activities and are organically linked together [10]. For example, emotion can affect the selectivity and enthusiasm of cognition, promote the deepening of cognition, and is conducive to the internalization of cognition [11]. At the same time, cognitive performance can be reflected through facial expressions. Cognition is the foundation of emotion, helping people understand and evaluate objective things; emotion is the subjective experience of understanding and evaluating these things, which affects people's attitudes. In this paper, cognitive emotion is defined as the subjective feeling of whether the current state meets one's own needs or requirements in activities where there is a certain level of cognitive demand. Cognitive emotion recognition is the perception and confirmation of the current cognitive state and feelings. Visual cognitive emotion recognition is an important way to study human reliability, and Facial Expression Recognition (FER) also plays an important role in human-computer interactions [12]. At present, FER methods have some disadvantages and bottlenecks [13] including (1) imbalanced development, with most methods based on static images and video-based methods mostly using deep learning based methods, such as 3D ConvNet [14] and ConvNet-RNN [15]; (2) the lack of valuable video training datasets and the class imbalance of in-the-wild databases; and (3) the low efficiency and quality of video-based facial expression feature extraction. In response to the above disadvantages and bottlenecks, this study classified cognitive emotions by analyzing people's emotional state and the relationship between emotion and cognition; studied an effective cognitive emotion feature extraction method based on dynamic videos; and proposed a sample recommendation and selection algorithm based on active learning to train a cognitive emotion recognition model. The entire method can accurately and efficiently identify human cognitive emotions in real time.