Detection and Classification Expression of Visual Information Based on Artificial Intelligent Review Work

Abstract

Human Emotion Expression is difficult to define and categorize, making it a difficult research topic. Teaching machines to decipher facial expressions is difficult. Dynamic facial expressions include modest muscular movements that shape their appearance. Advanced algorithms and methods are needed to capture and analyze these minute differences. Deep learning's popularity has expanded emotion recognition applications. The ability to recognize facial expressions has several practical uses. In the medical field, it can help doctors better understand their patients' mental health issues and formulate effective treatment plans. Emotion recognition may be used in the field of customer service to get a feel for how satisfied customers are and adjust strategies accordingly. User experiences may be improved through human-computer interaction when emotion detection allows for more natural and individualized interactions with technology. Gaming and entertainment could employ emotional reaction recognition to tailor and engage players based on their moods. Emotion detection can also improve situational awareness and identify high-stress situations in law enforcement and security. Recognition of pupils' facial expressions can assist teachers in improving classroom instruction and comprehending their emotions. Deep learning models provide an advantage over prior emotion-recognition systems, but they need additional research for high-robust expression detection.

Keywords

• Emotion Expression, Face Detection, Human Emotion Analysis, CNN Algorithm, Static and Dynamic expression