Theory, Practical Concepts, Strategies and Methods for Emotion Recognition

Predicting human behavior in strategic circumstances is an important aspect of intelligence, especially when it comes to real-world situations. Computer vision and high-tech facial recognition are useful for understanding human behavior better. Because complex emotions are intimately tied to human behavior, we focus on human behavior analysis using real-time emotion detection techniques in this proposed research. In this chapter, we provide a complete analysis of theory, practical concepts, strategies, and methods for emotion recognition. We present two methods, DeepFace and convolutional neural network (CNN)-based approach, for emotion recognition. The identification of human emotions is crucial in interpersonal relationships. Automatic emotion recognition is an emerging topic. As a result, there have been significant advancements in this sector. Traditional human behavior recognition is mostly based on global features of digital images. Emotions are expressed through voice communication, body gestures, hand poses, and facial expressions. Based on the information in the image, through the detection, extraction, and recognition pattern, the human emotion is understood. It is worth noting that a person’s emotion conceals a wealth of information, which is why it is necessary to model and then recognize human behavior through emotion analysis via facial recognition using computer vision. For computer vision techniques, the OpenCV library, dataset, and Python programming are employed. OpenCV is a large open-source library for computer vision-based algorithms. It helps us to develop a system that can process images and real-time video using computer vision. OpenCV focuses on image processing and real-time video capturing to detect faces and objects. Some emotion detection follows the human face in detection, feature analysis, operations on database, and support of keras models. The choice and analysis of methods, exploration, and analysis of machine learning algorithms for facial expression emotion recognition is a necessary topic in research in the emotion recognition area. CNNs are also a significant candidate because of their excellent generalization properties: for example, the DeepFace framework because of its exceptional accuracy and the Fisher face algorithm because of its simplicity have provided a base for emotion recognition with image processing. Appropriate behavior can be inferred from a variety of emotional sequences. Emotion recognition using a CNN model can be trained to evaluate photos and recognize facial emotion. Many algorithms, such as Haar cascade and Face Emotion Recognition, are popular because they can record spatial information; they are ideal for image recognition applications. Due to their vast number of filters, the inputs have unique characteristics. CNNs are used in the field of facial expression recognition because of their superior generalization properties. Emotion detection based on deep learning gives better performance than traditional methods with image processing. Emotion detection with sensors and sensing parameters with different physio-biological parameters is also a popular method. Applications of emotion detection can be developed with the possibility of predicting future events based on certain emotions, such as predicting an accident before it occurs based on the driver’s emotion or whether or not a customer will purchase a product, understanding how someone feels while standing on the edge of a railway platform, or defusing a crisis before it escalates. This could have a profound influence on safeguarding others. The proposed techniques provide theory and algorithms to application study for behavioral analysis. Thus, this chapter leads to an efficient approach to human behavior prediction, which can have infinite real-time applications in the academic as well as the industrial domain.