Study and Prediction of Human Behavior Based on Face-Recognition

The question relating to the Detection and Prediction of human behavior using Artificial Intelligence tools is the main focus of our research. More specifically, we have studied the link between emotion and human behavior in order to model an artificial intelligence that is not only capable of detecting but also, and above all, predicting human behavior. Predicting what someone is about to do next based on their body language is natural for humans, but not for computers. When we meet another person, they may greet us with a handshake or a fist bump. However, we don’t know which gesture will be used, but we can read the situation and react appropriately. In a new study, researchers at Columbia Engineering unveil a computer vision technique to give machines a more intuitive sense of what will happen next by taking advantage of higher-level associations between people, animals and objects. In order to validate the scientific reliability of the results emanating from the various theories, this article will follow a constructivist approach. Constructivism is an epistemological position based on the relativity of the notion of truth or reality. Reality is defined by the representation of a subject’s experience of reality. As Edgar Morin so aptly put it, “subject and object are indissociable, but our way of thinking excludes one through the other” [1]. This is the “dialogical” principle, the maintenance of duality within unity. Constructivism is based on subject-object interaction, with research “no longer defined by its object, but by its project” [2]. The model we have chosen, constructivism, is a reliable one, especially as it takes into account the three elements needed to assess the quality of an artificial intelligence model. The training data must be of good quality, the algorithm chosen must be relevant and robust, and the prediction error of the model generated must be as low as possible. In this article, we have shown that there is an intrinsic link between emotion and human behavior. Thus, understanding emotion would increase the possibility of success in predicting the behavior that should follow. Through literature survey and data analysis in related fields, it is found that emotion acquisition will be carried out using a facial feature algorithm for point capture and combined with machine learning for detection, analysis and prediction. This is why, when studying an individual’s facial features, it is possible to combine several techniques such as the HoG vector, the convolution matrix or the Gabor filter in order to identify the essential features of the face. This will enable us to determine the individual’s psychological state and, consequently, predict the possible behavior they may display. Applying this to the field of security would seem to be one of the best ways of curbing crime, especially in public places. To do this, you need the logistics to capture several photos and process them in real time.