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%.

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.

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.

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.  

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.

Computer Intelligent Nursing Care of Critically III Patients Based on Improved Patient Facial Expression Recognition Method

2014 ◽  
Vol 543-547 ◽  
pp. 2350-2353
Author(s):  
Xiao Yan Wan
Keyword(s):  
Facial Expression ◽  
Critically Ill ◽  
Nursing Care ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Appearance Model ◽  
Expression Recognition ◽  
Recognition Method ◽  
The Face

In order to extract the expression features of critically ill patients, and realize the computer intelligent nursing, an improved facial expression recognition method is proposed based on the of active appearance model, the support vector machine (SVM) for facial expression recognition is taken in research, and the face recognition model structure active appearance model is designed, and the attribute reduction algorithm of rough set affine transformation theory is introduced, and the invalid and redundant feature points are removed. The critically ill patient expressions are classified and recognized based on the support vector machine (SVM). The face image attitudes are adjusted, and the self-adaptive performance of facial expression recognition for the critical patient attitudes is improved. New method overcomes the effect of patient attitude to the recognition rate to a certain extent. The highest average recognition rate can be increased about 7%. The intelligent monitoring and nursing care of critically ill patients are realized with the computer vision effect. The nursing quality is enhanced, and it ensures the timely treatment of rescue.

A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

scholarly journals A Facial Expression Recognition Method Using Improved Capsule Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yifeng Zhao ◽  
Deyun Chen
Keyword(s):  
Facial Expression ◽  
Network Model ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Regression Tree ◽  
Gaussian Process Regression ◽  
Learning Technology ◽  
Expression Recognition ◽  
Recognition Method ◽  
The Face

Aiming at the problem of facial expression recognition under unconstrained conditions, a facial expression recognition method based on an improved capsule network model is proposed. Firstly, the expression image is normalized by illumination based on the improved Weber face, and the key points of the face are detected by the Gaussian process regression tree. Then, the 3dmms model is introduced. The 3D face shape, which is consistent with the face in the image, is provided by iterative estimation so as to further improve the image quality of face pose standardization. In this paper, we consider that the convolution features used in facial expression recognition need to be trained from the beginning and add as many different samples as possible in the training process. Finally, this paper attempts to combine the traditional deep learning technology with capsule configuration, adds an attention layer after the primary capsule layer in the capsule network, and proposes an improved capsule structure model suitable for expression recognition. The experimental results on JAFFE and BU-3DFE datasets show that the recognition rate can reach 96.66% and 80.64%, respectively.

CMAC WITH CLUSTERING MEMORY AND ITS APPLICATION TO FACIAL EXPRESSION RECOGNITION

2011 ◽  
Vol 25 (07) ◽  
pp. 1055-1072 ◽  
Author(s):  
YU-YI LIAO ◽  
JZAU-SHENG LIN ◽  
SHEN-CHUAN TAI
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Expression Recognition ◽  
Test Image ◽  
Frontal View ◽  
Still Images ◽  
Dct Coefficients ◽  
Block Sizes

In this paper, a facial expression recognition system based on cerebella model articulation controller with a clustering memory (CMAC-CM) is presented. Firstly, the facial expression features were automatically preprocessed and extracted from given still images in the JAFFE database in which the frontal view of faces were contained. Next, a block of lower frequency DCT coefficients was obtained by subtracting a neutral image from a given expression image and rearranged as input vectors to be fed into the CMAC-CM that can rapidly obtain output using nonlinear mapping with a look-up table in training or recognizing phase. Finally, the experimental results have demonstrated recognition rates with various block sizes of coefficients in lower frequency and cluster sizes of weight memory. A mean recognition rate of 92.86% is achieved for the testing images. CMAC-CM takes 0.028 seconds for test image in testing phase.

Facial Expression Recognition Based on Gabor Texture Features and Centre Binary Pattern

2015 ◽  
Vol 742 ◽  
pp. 257-260 ◽  
Author(s):  
Li Sai Li ◽  
Zi Lu Ying ◽  
Bin Bin Huang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Wavelet Filters ◽  
Adaboost Algorithm ◽  
Gabor Feature

This paper was proposed a new algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Centre Binary Pattern (CBP). Firstly, gabor texture feature were extracted from every expression image. Five scales and eight orientations of gabor wavelet filters were used to extract gabor texture features. Then the CBP features were extracted from gabor feature images and adaboost algorithm was used to select final features from CBP feature images. Finally, we obtain expression recognition results on the final expression features by Sparse Representation-based Classification (SRC) method. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm had a much higher recognition rate than the traditional algorithms.

scholarly journals Facial expression recognition based on improved VGG convolutional neural network

2021 ◽  
Vol 2083 (3) ◽  
pp. 032030
Author(s):  
Cui Dong ◽  
Rongfu Wang ◽  
Yuanqin Hang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Original Network ◽  
Feature Information ◽  
Fully Connected ◽  
A Current

Abstract With the development of artificial intelligence, facial expression recognition based on deep learning has become a current research hotspot. The article analyzes and improves the VGG16 network. First, the three fully connected layers of the original network are changed to two convolutional layers and one fully connected layer, which reduces the complexity of the network; Then change the maximum pooling in the network to local-based adaptive pooling to help the network select feature information that is more conducive to facial expression recognition, so that the network can be used on the facial expression datasets RAF-DB and SFEW. The recognition rate increased by 4.7% and 7% respectively.

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