Emotion Recognition and Object Tracking Methodological Survey

Emotion recognition enables machines to understand and respond to human emotions, leading to more natural and intuitive human-computer interactions . It can help improve user experiences in various domains such as virtual assistants, video games, customer services and social robots. The study aims to contribute valuable insights into the effectiveness of object tracking and recognition techniques, in improving behavioral recognition systems. It entails the analysis of various object detection and tracking techniques, alongside gesture and emotion recognition, data preprocessing, feature extraction techniques, and the use of Kinect sensors. Extraction of emotion-related information re-quires precise surveillance of facial characteristics and body movements. The study on doing analysis in low-light situations is included in the paper. The findings of this study advance the area of behavior analysis by shedding light on the efficacy of various object tracking techniques. The outcomes show how YOLO based tracking approaches can improve emotion recognition systems in comparison with other recognition algorithms and the capability of choosing the required algorithm based on one’s specifications. The study also emphasizes how important accurate object tracking is for obtaining the complex facial and bodily cues required for successful emotion identification.