A REVIEW ON AUTOMATIC FACIAL EXPRESSION RECOGNITION SYSTEMS

Computer Science and Information Technologies

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.

2012

Facial expression recognition has potential applications in different aspects of day-today life not yet realized due to absence of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction in combination feedforward neural networks (classifier) for recognition of seven different facial expressions from still pictures of the human face. The study presented here gives simple method in facial expression recognition. The study presented here gives 60% to 70 % recognition of facial expression for the entire database of JAFEE. In this study the Japanese Female Facial Expression (JAFFE) database used which contains expressers that expressed expressions. The present study proves the feasibility of computer vision based facial expression recognition for practical applications like surveillance and human computer interaction.

Signal, Image and Video Processing, 2012

In this paper, we investigate feature extraction and feature selection methods as well as classification methods for automatic facial expression recognition (FER) system. The FER system is fully automatic and consists of the following modules: face detection, facial detection, feature extraction, selection of optimal features, and classification. Face detection is based on AdaBoost algorithm and is followed by the extraction of frame with the maximum intensity of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features using different methods including: Gabor filters, log Gabor filter, local binary pattern (LBP) operator, higher-order local autocorrelation (HLAC) and a recent proposed method called HLAC-like features (HLACLF). The most informative features are selected based on both wrapper and filter feature selection methods. Experiments on several facial expression databases show comparisons of different methods.

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012

Facial expression analysis is one of the popular fields of research in human computer interaction (HCI). It has several applications in next generation user interfaces, human emotion analysis, behavior and cognitive modeling. In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns(LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA). The algorithm is implemented in real time for expression classification since the computational complexity of the algorithm is small. A customizable approach is proposed for facial expression analysis, since the various expressions and intensity of expressions vary from person to person. The system uses grayscale frontal face images of a person to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger. Index Terms--Facial expression analysis, Haar classifier, Principal Component Analysis(PCA), multi-block Local Binary Patterns(LBP) features.

International Journal of Digital Content Technology and its Applications

Automatic facial expression analysis is vital in identifying the emotional state of a human being. Hence it is an essential tool for human-computer interaction. Success of a facial expression recognition system largely depends on selecting suitable and effective facial features. This paper proposes a novel approach for recognizing facial expression where facial feature is represented by completed local binary pattern (CLBP) applied on Gabor filtered facial image. Expression images are classified into prototype expression via support vector machine (SVM) with different kernels. Experiments performed on Cohn-Kanade facial expression database obtained a recognition rate of above 97% for seven-class expression set indicating superiority of the proposed method compared to other methods.

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.

Facial expression recognition is gaining widespread importance as the applications related to Human-Computer interactions are increasing. This paper mentions various techniques and approaches that have been used in the field of facial expression recognition. Facial expression recognition takes place in various stages and these stages have been implemented by various approaches. Viola and Jones for face detection, Gabor filters for feature extraction, SVM classifiers for classification, L1 minimization for sparse representation, facial expression recognition, geometric deformation model, multiple gabor filters for robust feature extraction, parallel implementation of Viola and Jones for face detection and parallel implementation of SVM classifier for classification of expressions are discussed in this paper.

2011

Facial Expression Recognition is necessary for designing any human-machine interface. The main issue of Facial Expression Recognition is to decide what features are required to represent a Facial Expression. In this paper, we propose the hybrid technique for facial expression recognition. In this paper we proposed a combined method of feature extraction using Discrete Cosine Transform, Gabor Filter, Wavelet Transform and Gaussian distribution to improve the recognition rate. Experimental are performed on seven expressions, (anger, disgust, fear, happiness, sadness, surprise, neutral ) of JAFFE dataset. The result of Proposed work is compared with result of individual Feature Extraction Techniques that show that Facial Expression Recognition Rate can be improved by combining best features of DCT, Gabor Filter, Wavelet Transform and Gaussian Distribution.

2012

Abstract:-The purpose of this paper is to present a comprehensive study of latest and most famous facial features extraction techniques and as well as classification techniques. We studied these techniques in two different perspectives; one is spatial domain and other is frequency domain. We found many advantages and disadvantages of each technique inside one domain and as well in between different domains. We observed that Local Binary Pattern is a new technique and now it is becoming very famous technique in spatial ...

A wide range of applications exist for Facial Expression Recognition (FER) like neutral, sadness, surprise, happiness, fear, anger, contempt and disgust, which include Emotional / Mental state recognition, stress and anxiety detection in medial domain, security domain, human computer interaction etc. In computer vision field Facial Expression Recognition is very interesting and challenging area. In this paper, review some of the facial expression recognition methods are presented. The feature extraction techniques play a crucial role. In this paper a few Facial Feature Extraction techniques like Local Binary Pattern, Local Directional Pattern are discussed. Also recognition based on support vector machines and deep learning algorithms are discussed and compared.