Automation of non-verbal communication of facial expressions

2020

Emotions are very present in our daily life. They are manifested by physiological reactions (palpitations, heat, and acceleration of the pulse), motor reactions (gestural expressions, facial expressions) and changes in the voice. The complexity of emotions was capable to seek attention of various researchers. Differing representations of emotions were then proposed to produce a set of theories. The automation of facial expression detection can then be approached in a variety of ways. We concentrated on the face expression detection. Concerning to this the recognition is based on different visual characteristics of the expression. This paper is dedicated at first to the exhibition of the definition of emotions, followed by the great theories that have emerged in this area. In a second step, we present the methods of extraction of the facial characteristics and the methods of selection. Finally, some databases used for automatic recognition of emotions are presented.

In this paper, we have provided a comprehensive review of modern facial expression recognition system. The history of the technology as well as the current status in terms of accomplishments and challenges has been emphasized. First, we highlighted some modern applications of the technology. The best methods of face detection, an essential component of automatic facial expression system, are also discussed. Facial Action Coding Systems-the cumulative database of research and development of micro expressions within the behavioral science are also enlightened. Then various facial expression databases and the types of recognitions are explained in detail. Finally, we provided the procedures of facial expression recognition from feature extraction to classifications, emphasizing on modern and best approaches. Then the challenges encountered when comparing results with others are highlighted and suggestions to alleviate the problems, provided.

Facial expressions give important information about emotions of a person. Understanding facial expressions accurately is one of the challenging tasks for interpersonal relationships. Automatic emotion detection using facial expressions recognition is now a main area of interest within various fields such as computer science, medicine, and psychology. HCI research communities also use automated facial expression recognition system for better results. Various feature extraction techniques have been developed for recognition of expressions from static images as well as real time videos. The expression recognition model is oriented on the Facial Action Coding System (FACS). The hard constraints on the scene processing and recording conditions set a limited robustness to the analysis. A probabilistic oriented framework is used in order to manage the uncertainties and lack of information. The support for the specific processing involved was given through a Multimodal data fusion platform. The Bayesian network is used to encode the dependencies among the variables. The temporal dependencies are to be extracted to make the system be able to properly select the right expression of emotion. In this way, the system is able to overcome the performance of the previous approaches that dealt only with prototypic facial expression.

Face and Gesture 2011, 2011

We present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different protoypical facial expressions. It also estimates the locations of 10 facial features as well as the 3-D orientation (yaw, pitch, roll) of the head. On a database of posed facial expressions, Extended Cohn-Kanade (CK+ [1]), CERT achieves an average recognition performance (probability of correctness on a two-alternative forced choice (2AFC) task between one positive and one negative example) of 90.1% when analyzing facial actions. On a spontaneous facial expression dataset, CERT achieves an accuracy of nearly 80%. In a standard dual core laptop, CERT can process 320 × 240 video images in real time at approximately 10 frames per second.

Handbook of Face Recognition, 2005

Handbook of Ambient Intelligence and Smart Environments, 2010

Facial expressions, resulting from movements of the facial muscles, are the face changes in response to a person's internal emotional states, intentions, or social communications. There is a considerable history associated with the study on facial expressions. Darwin (1872) was the first to describe in details the specific facial expressions associated with emotions in animals and humans, who argued that all mammals show emotions reliably in their faces. Since that, facial expression analysis has been a area of great research interest for behavioral scientists . Psychological studies suggest that facial expressions, as the main mode for non-verbal communication, play a vital role in human face-to-face communication. For illustration, we show some examples of facial expressions in .

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012

This paper focus on understanding human visual system when it decodes or recognizes facial expressions. Results presented can be exploited by the computer vision research community for the development of robust descriptor based on human visual system for facial expressions recognition. We have conducted psycho-visual experimental study to find which facial region is perceptually more attractive or salient for a particular expression. Eye movements of 15 observers were recorded with an eye-tracker in free viewing conditions as they watch a collection of 54 videos selected from Cohn-Kanade facial expression database, showing six universal facial expressions. The results of the study shows that for some facial expressions only one facial region is perceptually more attractive than others. Other cases shows the attractiveness of two to three facial regions. This paper also proposes a novel framework for automatic recognition of expressions which is based on psycho-visual study.

2020

Received May 10, 2020 Revised Jun 1, 2020 Accepted Jul 24, 2020 Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial expression recognition system (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face’s components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.

Computer Science and Information Technologies, 2021

Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial expression recognition system (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face's components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.