Abstract
To solve the problem of emotional loss in teaching and improve the teaching effect, an intelligent teaching method based on facial expression recognition was studied. The traditional active shape model (ASM) was improved to extract facial feature points. Facial expression was identified by using the geometric features of facial features and support vector machine (SVM). In the expression recognition process, facial geometry and SVM methods were used to generate expression classifiers. Results showed that the SVM method based on the geometric characteristics of facial feature points effectively realized the automatic recognition of facial expressions. Therefore, the automatic classification of facial expressions is realized, and the problem of emotional deficiency in intelligent teaching is effectively solved.
Highlights
With the development of information intelligence technology, artificial intelligence education faces challenges and opportunities
Results showed that the support vector machine (SVM) method based on the geometric characteristics of facial feature points effectively realized the automatic recognition of facial expressions
The proposed geometric characteristics and SVM classification methods are introduced in detail
Summary
With the development of information intelligence technology, artificial intelligence education faces challenges and opportunities. Multimedia computers are widely used in the field of education, which has a great impact on the traditional teaching process. The intelligent computer-aided teaching system is integrated with network, artificial intelligence and multimedia technology. It differs from traditional computer-aided teaching systems. The distinguishing feature is its intelligent and personalized teaching function, which has the advantages of interactivity, sharing, autonomy and efficiency. In the process of learning, human-computer interaction is realized. Teachers and students can realize the interaction between teaching and learning through the network. Combined with the geometric features based on facial features, the SVM method is used to identify facial expressions.
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