摘要：Cognitive state learning is regarded as a crucial aspect, which decides learning efficiency in class, and analyzing the cognitive state of the learners has emerged as a hot topic in the research field and it is a huge issue in education domain. Here huge amount of data plays a crucial role in the learner’s cognitive state recognition of educators quantitatively using a massive amount of time series data and this facilitates individual attention to improve the outputs of learning. The emotions are not concentrated utmost in all prevailing cognitive state analysis approaches. Therefore, the emotion estimation learning has its significance on cognitive state analysis. Hence, automated facial expression recognition (FER) shows a clear explanation for emotion recognition. As per psychological theory, the emotional states of an individual can be categorized into six important groups, which include surprise, fear, disgust, anger, happiness and sadness. Automated extraction of these emotions from the facial images can be very useful in learning the cognitive state analysis. The complicated features learning and pattern classification is chiefly attained by utilizing Machine learning algorithms and merely deep neural network. This paper focuses on cognitive state analysis using various methodologies which is based on deep learning techniques in intelligent classroom.
关键词：Emotion estimation;Cognitive state analysis;Facial recognition;E-learn
