EMOTION DETECTION WITH DEEP LEARNING AND OPEN CV BASED VEHICLE AUTOMATION FOR SELF DRIVING CARS

Facial emotion detection (FED) is a fascinating field of study that involves the use of technology to recognize emotions from facial expressions. This paper provides an overview of facial emotion detection, discussing its importance, methods, and applications in everyday life. We explore how machine learning algorithms analyze facial features to identify emotions such as happiness, sadness, anger, surprise, fear, and disgust. Additionally, we discuss the challenges and future prospects of facial emotion detection technology. Facial emotion analysis is efficiently used in surveillance videos, expression analysis, gesture recognition, smart homes, computer games, depression treatment, patient monitoring, anxiety, detecting lies, psychoanalysis, paralinguistic communication, detecting operator fatigue and robotics. Image processing is a method to convert an image into digital form and perform some operation on it. This is done to enhance the image or to extract useful information from it. There are seven basic expressions which include: angry, disgusted, fearful, happy, sad, neutral, surprised. So it is very important to detect these emotions on the face. A human-computer interaction system for an automatic face recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. The research evaluates how perfectly models recognize emotions using datasets to measure performance in terms of accuracy and overall effectiveness.