Facial Expression Recognition: A Survey

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023

Facial expression recognition (FER) is a rapidly emerging topic in computer vision that has gotten a lot of interest because of its numerous applications in fields including psychology, sociology, human-computer interaction (HCI), and security. FER seeks to recognise and analyse human facial expressions in order to determine emotions and other mental states. Several strategies, including feature-based, kernel-based, and deep learning-based methods, have been developed and implemented in FER in recent years. FER's major goal is to extract and identify the most discriminating elements that accurately represent the emotions expressed by facial expressions. The literature reviewed in this field shows that deep learning-based methods have outperformed traditional feature-based and kernel-based methods in terms of accuracy and robustness in recognizing facial expressions. However, these deep learning-based methods also pose several challenges, such as the need for large labeled-data-sets, robustness to different facial poses and illumination conditions, and generalization to unseen data. Despite these challenges, the field of FER is expected to continue growing, and future research will likely focus on addressing these challenges and improving the accuracy and robustness of FER systems.

2020

Face expressions play most significant role to understand intention of others.’ Now a day face expression recognition has become very important and interesting topic for research. It is very important to interact human and machine. Human facial expressions varies according to age and cultures, so it is difficult to recognize human expressions. To recognize theexpression of a human face first it is required to identify the distinctive facial highlights, for example, the developments of eye, nose, lips, and so forth and afterward characterize them contrasting and prepared information utilizing a reasonable classifier for expression recognition. There are many different types of techniques used to recognition of face expression. This paper present the most important technique for feature extraction and classification to recognize face expressions . Moreover, reduce the information measurement by choosing the exceptionally discriminative features. The proposed structure is fit for givin...

Computer vision is one among the thrust research area in the field of Image processing. Facial expression recognition is one among the thrust research dimension in computer vision. The process of recognition and identification is important due to the similarity of facial expressions. The motivation behind this research area is its capability to resolve an image processing problem and its wide range of applications. The main rationale of all images processing and computer vision algorithms is to build the visual data in a useful manner. For this reason in the domain of computer vision, the facial expression recognition begins with its applications in the HCI (Human Computer Interaction) where visual look of human, sight and touch sensations (also known as moods) and voice are utilized at the same time. This research article reviews several literatures pertaining to facial expression recognition.

International Journal of ADVANCED AND APPLIED SCIENCES, 2017

In this era of technology, we need applications which could be easy to use and are user-friendly that even people with certain disabilities could use them easily. Many applications exist for human behavior understanding, detection of mental disorders, and synthetic human expressions in the domain of automatic facial recognition systems. Generally, most of the publications propose two methods for automatic Facial Expression Recognition (FER) systems i.e. geometric based and appearance based approach. Much work has been done on the static analysis where facial expression recognition had been performed on still images. While facial expressions are naturally dynamic, they are not easy to detect so the focus of the study is now shifted to find new methods which would be helpful to improve accuracy, lower computational cost, and less memory consumption. This paper demonstrates a quick survey of facial expression recognition by analyzing various algorithms; evaluated by comparing their results in general which in turn broadened the scope for other researchers they could efficiently offer a solution to related problems.

expression recognition (happy, sad, disgust, surprise, angry, fear expressions) is application of advanced object detection, pattern recognition and classification task. Facial expression recognition techniques detecting emotion of people' using their facial expressions. This has found applications in technical fields such as Human-computer-Interaction (HCI) and security monitoring. It generally requires fast processing and decision making. Therefore, it is imperative to develop innovative recognition methods that can detect facial expressions effectively and efficiently. Although humans recognize facial expressions virtually without efforts or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of emotional state, machine must be taught to understand facial gestures. This paper focuses on a review of different techniques for face recognition, face detection and emotion recognition are presented.

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.

The automatic recognition of facial expressions has been an active research topic since the early nineties. There have been several advances in the past few years in terms of face detection and tracking, feature extraction mechanisms and the techniques used for expression classification. This paper surveys some of the published work since 2001 till date. The paper presents a time-line view of the advances made in this field, the applications of automatic face expression recognizers, the characteristics of an ideal system, the databases that have been used and the advances made in terms of their standardization and a detailed summary of the state of the art. The paper also discusses facial parameterization using FACS Action Units (AUs) and MPEG-4 Facial Animation Parameters (FAPs) and the recent advances in face detection, tracking and feature extraction methods. Notes have also been presented on emotions, expressions and facial features, discussion on the six prototypic expressions and the recent studies on expression classifiers. The paper ends with a note on the challenges and the future work. This paper has been written in a tutorial style with the intention of helping students and researchers who are new to this field.

International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016

Facial expression recognition performs a critical role in the human-machine interaction area. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Facial Expression Recognition system is used in security. During the past years recognition of face has received most important attention as one of the most significant image application understanding and analysis. Many algorithms have been implemented on different static and non static conditions (uncontrolled conditions).Static conditions include static and uniform background, identical poses, similar illumination, neutral frontal face .Non static conditions include position, partial occlusion orientation; varying lighting conditions and facial hair, which makes recognition process a complex problem. Facial expression recognition is a example where computer and humans underperforms. It has most importance for the video retrieval and video based management classification. It may be used in behavior psychology and science.

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.

The primary goal of this paper is to make a comparative study on various approaches used to identify the person's face and thereafter recognize their emotion. Among the various techniques implemented, Neural Networks, Hidden Markov Model and Dimensionality reduction techniques have received lot of attention. The face and facial emotion recognition system would require care and efforts in data acquisition, pre-processing, feature extraction, classification and performance evaluation. The main aim of this review paper is to study and compare the well-known techniques used at different stages to recognize the face and its emotion. different gender, race and features. These varieties in the database such as glasses, moustache and beard had enabled the system to learn to differentiate various levels of facial expressions successfully.