From Face to Facial Expression

2010 ◽  
pp. 217-238
Author(s):  
Zakia Hammal ◽  
Zakia Hammal
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Everyday Life ◽  
Facial Expression Recognition ◽  
Multidisciplinary Approach ◽  
Expression Recognition ◽  
Future Challenges ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Processing Steps

This chapter addresses recent advances in computer vision for facial expression classification. The authors present the different processing steps of the problem of automatic facial expression recognition. They describe the advances of each stage of the problem and review the future challenges towards the application of such systems to everyday life situations. The authors also introduce the importance of taking advantage of the human strategy by reviewing advances of research in psychology towards multidisciplinary approach for facial expression classification. Finally, the authors describe one contribution which aims at dealing with some of the discussed challenges.

From Face to Facial Expression

2013 ◽  
pp. 1508-1531
Author(s):  
Zakia Hammal
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Everyday Life ◽  
Facial Expression Recognition ◽  
Multidisciplinary Approach ◽  
Expression Recognition ◽  
Future Challenges ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Processing Steps

This chapter addresses recent advances in computer vision for facial expression classification. The authors present the different processing steps of the problem of automatic facial expression recognition. They describe the advances of each stage of the problem and review the future challenges towards the application of such systems to everyday life situations. The authors also introduce the importance of taking advantage of the human strategy by reviewing advances of research in psychology towards multidisciplinary approach for facial expression classification. Finally, the authors describe one contribution which aims at dealing with some of the discussed challenges.

A Design of Face Detection and Facial Expression Recognition Techniques Based on Boosting Schema

2011 ◽  
Vol 121-126 ◽  
pp. 617-621 ◽  
Author(s):  
Chang Yi Kao ◽  
Chin Shyurng Fahn
Keyword(s):  
Facial Expression ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Extraction Procedure ◽  
Expression Recognition ◽  
Classification Procedure ◽  
Facial Expression Classification ◽  
Human Faces ◽  
Expression Classification

During the development of the facial expression classification procedure, we evaluate three machine learning methods. We combine ABAs with CARTs, which selects weak classifiers and integrates them into a strong classifier automatically. We have presented a highly automatic facial expression recognition system in which a face detection procedure is first able to detect and locate human faces in image sequences acquired in real environments. We need not label or choose characteristic blocks in advance. In the face detection procedure, some geometrical properties are applied to eliminate the skin color regions that do not belong to human faces. In the facial feature extraction procedure, we only perform both the binarization and edge detection operations on the proper ranges of eyes, mouth, and eyebrows to obtain the 16 landmarks of a human face to further produce 16 characteristic distances which represent a kind of expressions. We realize a facial expression classification procedure by employing an ABA to recognize six kinds of expressions. The performance of the system is very satisfactory; whose recognition rate achieves more than 90%.

scholarly journals Three Level Optimization Models of Scaled Gabor Features for Facial Expression Recognition

2018 ◽  
Vol 7 (2.24) ◽  
pp. 348
Author(s):  
Neha . ◽  
Pratistha Mathur
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Feature Reduction ◽  
Expression Recognition ◽  
Texture Pattern ◽  
Average Recognition Rate ◽  
Expression Classification

The area of computer vision and machine learning for pattern recognition has witnessed the need for research for the development of algorithms for different applications such as human-computer interaction, automated access control and surveillance. In the field of computer vision Facial Expression Recognition has attracted the researcher’s interest. This paper presents a novel feature extraction technique: Gabor-Average-DWT-DCT for automatic facial expression recognition from a person's face image invariant of illumination. Facial Emotions have different edge and texture pattern. Gabor filter is able to extract edges and texture pattern of faces but with problem of huge dimension and high redundancy. The problem of huge dimension and high redundancy is reduced by proposed Average-DWT-DCT feature reduction technique in order to increase accuracy of system. Proposed Gabor- Average -DWT-DCT provides a compact feature vector for reducing response time of system compared to existing Gabor based expression classification. Detailed quantitative analysis is done and results that the average recognition rate of proposed technique is better than state of art results.  

scholarly journals Facial Expression Recognition Using Computer Vision: A Systematic Review

2019 ◽  
Vol 9 (21) ◽  
pp. 4678 ◽  
Author(s):  
Daniel Canedo ◽  
António J. R. Neves
Keyword(s):  
Systematic Review ◽  
Computer Vision ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Future Research ◽  
Expression Recognition ◽  
Contemporary World

Emotion recognition has attracted major attention in numerous fields because of its relevant applications in the contemporary world: marketing, psychology, surveillance, and entertainment are some examples. It is possible to recognize an emotion through several ways; however, this paper focuses on facial expressions, presenting a systematic review on the matter. In addition, 112 papers published in ACM, IEEE, BASE and Springer between January 2006 and April 2019 regarding this topic were extensively reviewed. Their most used methods and algorithms will be firstly introduced and summarized for a better understanding, such as face detection, smoothing, Principal Component Analysis (PCA), Local Binary Patterns (LBP), Optical Flow (OF), Gabor filters, among others. This review identified a clear difficulty in translating the high facial expression recognition (FER) accuracy in controlled environments to uncontrolled and pose-variant environments. The future efforts in the FER field should be put into multimodal systems that are robust enough to face the adversities of real world scenarios. A thorough analysis on the research done on FER in Computer Vision based on the selected papers is presented. This review aims to not only become a reference for future research on emotion recognition, but also to provide an overview of the work done in this topic for potential readers.

scholarly journals Automatic Multiface Expression Recognition Using Convolutional Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 1-13
Author(s):  
Padmapriya K.C. ◽  
Leelavathy V. ◽  
Angelin Gladston
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Behavior Understanding ◽  
Human Behavior Understanding ◽  
Expression Classification ◽  
Machine Interaction

The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.

A Facial Expression Recognition Algorithm Based on Computer Vision

2013 ◽  
Vol 380-384 ◽  
pp. 4057-4060
Author(s):  
Lang Guo ◽  
Jian Wang
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Expression Recognition ◽  
Two Dimensional ◽  
Manifold Structure ◽  
Facial Expression Database

Analyzing the defects of two-dimensional facial expression recognition algorithm, this paper proposes a new three-dimensional facial expression recognition algorithm. The algorithm is tested in JAFFE facial expression database. The results show that the proposed algorithm dynamically determines the size of the local neighborhood according to the manifold structure, effectively solves the problem of facial expression recognition, and has good recognition rate.

scholarly journals A Facial Expression Recognition Model Based on Texture and Shape Features

2020 ◽  
Vol 37 (4) ◽  
pp. 627-632
Author(s):  
Aihua Li ◽  
Lei An ◽  
Zihui Che
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Shape Features ◽  
The Face ◽  
Facial Expression Classification

With the development of computer vision, facial expression recognition has become a research hotspot. To further improve the accuracy of facial expression recognition, this paper probes deep into image segmentation, feature extraction, and facial expression classification. Firstly, the convolution neural network (CNN) was adopted to accurately separate the salient regions from the face image. Next, the Gaussian Markov random field (GMRF) model was improved to enhance the ability of texture features to represent image information, and a novel feature extraction algorithm called specific angle abundance entropy (SAAE) was designed to improve the representation ability of shape features. After that, the texture features were combined with shape features, and trained and classified by the support vector machine (SVM) classifier. Finally, the proposed method was compared with common methods of facial expression recognition on a standard facial expression database. The results show that our method can greatly improve the accuracy of facial expression recognition.

scholarly journals Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted, Soft Epidermal Electronics

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Meiqi Zhuang ◽  
Lang Yin ◽  
Youhua Wang ◽  
Yunzhao Bai ◽  
Jian Zhan ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Real Life ◽  
Small Sample ◽  
Expression Recognition ◽  
Time Dynamic ◽  
Epidermal Electronics

The facial expressions are a mirror of the elusive emotion hidden in the mind, and thus, capturing expressions is a crucial way of merging the inward world and virtual world. However, typical facial expression recognition (FER) systems are restricted by environments where faces must be clearly seen for computer vision, or rigid devices that are not suitable for the time-dynamic, curvilinear faces. Here, we present a robust, highly wearable FER system that is based on deep-learning-assisted, soft epidermal electronics. The epidermal electronics that can fully conform on faces enable high-fidelity biosignal acquisition without hindering spontaneous facial expressions, releasing the constraint of movement, space, and light. The deep learning method can significantly enhance the recognition accuracy of facial expression types and intensities based on a small sample. The proposed wearable FER system is superior for wide applicability and high accuracy. The FER system is suitable for the individual and shows essential robustness to different light, occlusion, and various face poses. It is totally different from but complementary to the computer vision technology that is merely suitable for simultaneous FER of multiple individuals in a specific place. This wearable FER system is successfully applied to human-avatar emotion interaction and verbal communication disambiguation in a real-life environment, enabling promising human-computer interaction applications.

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