A Visualization Method of Facial Expression Deformation Based on Pore-Scale Facial Feature Matching

2019 ◽  
Author(s):  
Xiaorui Hu ◽  
Dong Li ◽  
Xianxian Zeng ◽  
Jingyi Lin ◽  
Yun Zhang
Keyword(s):  
Facial Expression ◽  
Feature Matching ◽  
Facial Feature ◽  
Visualization Method ◽  
Pore Scale

Research on the Facial Expression Feature Extraction of Facial Expression Recognition Based on MATLAB

2014 ◽  
Vol 1049-1050 ◽  
pp. 1522-1525
Author(s):  
Wang Ju ◽  
Ding Rui ◽  
Chun Yan Nie
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Facial Expression ◽  
Interpersonal Communication ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Facial Feature ◽  
Expression Recognition ◽  
Information Communication ◽  
Facial Feature Extraction

In such a developed day of information communication, communication is an important essential way of interpersonal communication. As a carrier of information, expression is rich in human behavior information. Facial expression recognition is a combination of many fields, but also a new topic in the field of pattern recognition. This paper mainly studied the facial feature extraction based on MATLAB, by MATLAB software, extracting the expression features through a large number of facial expressions, which can be divided into different facial expressions more accurate classification .

scholarly journals Automated Facial Expression Recognition Using Gradient-Based Ternary Texture Patterns

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Faisal Ahmed ◽  
Emam Hossain
Keyword(s):  
Facial Expression ◽  
Recognition Performance ◽  
Texture Feature ◽  
Recognition Task ◽  
Facial Feature ◽  
Recognition System ◽  
Feature Descriptor ◽  
Expression Recognition ◽  
Local Texture ◽  
Face Expression

Recognition of human expression from facial image is an interesting research area, which has received increasing attention in the recent years. A robust and effective facial feature descriptor is the key to designing a successful expression recognition system. Although much progress has been made, deriving a face feature descriptor that can perform consistently under changing environment is still a difficult and challenging task. In this paper, we present the gradient local ternary pattern (GLTP)—a discriminative local texture feature for representing facial expression. The proposed GLTP operator encodes the local texture of an image by computing the gradient magnitudes of the local neighborhood and quantizing those values in three discrimination levels. The location and occurrence information of the resulting micropatterns is then used as the face feature descriptor. The performance of the proposed method has been evaluated for the person-independent face expression recognition task. Experiments with prototypic expression images from the Cohn-Kanade (CK) face expression database validate that the GLTP feature descriptor can effectively encode the facial texture and thus achieves improved recognition performance than some well-known appearance-based facial features.

Research on Facial Feature Detection Based on Correlation Analysis

2014 ◽  
Vol 1044-1045 ◽  
pp. 1489-1493
Author(s):  
Ming Zhou ◽  
Shu He ◽  
Yong Jun Cheng
Keyword(s):  
Facial Expression ◽  
Feature Detection ◽  
Evaluation Method ◽  
Facial Feature ◽  
Expression Recognition ◽  
3D Images ◽  
Modified Method ◽  
Innovative Methods ◽  
Combined Features ◽  
2D And 3D

In order to enhance the extraction efficiency of facial feature, the paper explores a novel defect evaluation method that uses combined features and modified method classifiers to characterize and classify the defects of facial expression. It provides a good approach to implement facial expression recognition both in 2D and 3D images. Innovative methods which are aimed at reducing the computational complexity and improving the accuracy of expression recognition are proposed.The experiments result showed the proposed method achieved lower error rate than other method.

scholarly journals Analysis of Feature Extraction and Anti-Interference of Face Image under Deep Reconstruction Network Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jin Yang ◽  
Yuxuan Zhao ◽  
Shihao Yang ◽  
Xinxin Kang ◽  
Xinyan Cao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Bayesian Method ◽  
Feature Matching ◽  
Facial Feature ◽  
Face Image ◽  
Feature Representation ◽  
Image Feature ◽  
Training Time ◽  
Network Algorithm

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network (CNN) reconstructed by deep learning (DL) framework in face image feature extraction (FE) and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent (SGD) and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild (LFW) face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1 (accuracy: 99.03%, loss: 0.0047, training time: 352 s, and overfitting rate: 1.006), and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time (206.44 s) and a higher correct matching rate (88.75%) than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference.

Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier

2021 ◽  
pp. 76-113
Author(s):  
Manoj Prabhakaran Kumar ◽  
Manoj Kumar Rajagopal
Keyword(s):  
Facial Expression ◽  
Feature Vector ◽  
Expression System ◽  
Facial Feature ◽  
Recognition System ◽  
Support Vector ◽  
Detection And Tracking ◽  
Geometric Deformable Model ◽  
Vector Machines ◽  
The Face

This chapter proposes the facial expression system with the entire facial feature of geometric deformable model and classifier in order to analyze the set of prototype expressions from frontal macro facial expression. In the training phase, the face detection and tracking are carried out by constrained local model (CLM) on a standardized database. Using the CLM grid node, the entire feature vector displacement is obtained by facial feature extraction, which has 66 feature points. The feature vector displacement is computed in bi-linear support vector machines (SVMs) classifier to evaluate the facial and develops the trained model. Similarly, the testing phase is carried out and the outcome is equated with the trained model for human emotion identifications. Two normalization techniques and hold-out validations are computed in both phases. Through this model, the overall validation performance is higher than existing models.

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