scholarly journals Facial Micro-expression Recognition Algorithm Based on Big Data

2021 ◽  
Vol 2066 (1) ◽  
pp. 012023
Author(s):  
Qun Xia ◽  
Xiaofeng Ding
Keyword(s):  
Big Data ◽  
Dimensionality Reduction ◽  
Facial Expressions ◽  
New Technology ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Survey Method ◽  
Complex Data ◽  
Expression Recognition ◽  
Micro Expression

Abstract The 21st century is the era of big data. All aspects of society, from facial expressions to national defense and military, will generate massive amounts of data. Facial expression recognition technology, as a new technology spawned in the era of big data, has broad applications The prospects are widely used in intelligent transportation, assisted medical care, distance education, interactive games and public safety. In recent years, it has attracted more scholars’ attention and has become another research hotspot in the field of computer vision and machine learning. The purpose of this article is to study the facial micro-expression recognition algorithm based on big data. This time, big data technology is used to analyze the algorithm. Big data can better solve the small changes in face recognition and complex data processing. This paper firstly summarizes the basic theory of big data, derives the core technology of big data, and analyzes its shortcomings and shortcomings based on the current research status of facial micro-expression in my country, and finally discusses the big data based on big data. Research on facial micro-expression recognition algorithm under the following. This article takes the research situation of the face micro-expression recognition by related companies as the survey object, and analyzes it through the literature data method, questionnaire survey method, mathematical statistics method and other research methods. Experimental results show that the lower the dimensionality reduction, the less classification time is used. When the dimensionality reduction is 45 dimensions, the recognition rate of facial expressions is the highest.

scholarly journals Optical Flow Filtering-Based Micro-Expression Recognition Method

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2056
Author(s):  
Junjie Wu ◽  
Jianfeng Xu ◽  
Deyu Lin ◽  
Min Tu
Keyword(s):  
Optical Flow ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Recognition Method ◽  
Video Clips ◽  
Low Degree ◽  
Micro Expression

The recognition accuracy of micro-expressions in the field of facial expressions is still understudied, as current research methods mainly focus on feature extraction and classification. Based on optical flow and decision thinking theory, we propose a novel micro-expression recognition method, which can filter low-quality micro-expression video clips. Determined by preset thresholds, we develop two optical flow filtering mechanisms: one based on two-branch decisions (OFF2BD) and the other based on three-way decisions (OFF3WD). In OFF2BD, which use the classical binary logic to classify images, and divide the images into positive or negative domain for further filtering. Differ from the OFF2BD, OFF3WD added boundary domain to delay to judge the motion quality of the images. In this way, the video clips with low degree of morphological change can be eliminated, so as to directly improve the quality of micro-expression features and recognition rate. From the experimental results, we verify the recognition accuracy of 61.57%, and 65.41% for CASMEII, and SMIC datasets, respectively. Through the comparative analysis, it shows that the scheme can effectively improve the recognition performance.

scholarly journals An Improved Non-Face-to-Face, Contactless Preference Survey System

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
KwangCheol Rim
Keyword(s):  
Public Opinion ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Survey System ◽  
Election Results ◽  
Actual Response ◽  
Public Opinion Polls ◽  
Opinion Polls

Conventional election-related public opinion polls have utilized the automated response system (ARS) method. The ARS public opinion polls are predicated on the convenience of use and require random telephonic responses. However, the actual response rate is less than 5%. As a result, discrepancies between recent public opinion polls and the actual election results have become an issue. In this study, we propose a system that quantifies the preferences by region, age, and gender by quantifying emotions based on the behaviors and facial expressions of the citizens passing by at the campaign site and utilizes them as basic statistics. Furthermore, a previously published facial recognition artificial intelligence (AI) was used to obtain age, gender, and various facial recognition data, along with citizens’ emotions. The published facial recognition AI produced stability of over 99% recognition rate. The data structure followed a weighted reverse tree structure, and facial expressions, gender, and age were analyzed using the published facial recognition algorithm. Moreover, the expressions as well as the behaviors showing emotions were merged to gather and analyze data with weights.

scholarly journals Facial Expressions Recognition Based on Delaunay Triangulation of Landmark and Machine Learning

2021 ◽  
Vol 38 (6) ◽  
pp. 1575-1586
Author(s):  
Farid Ayeche ◽  
Adel Alti
Keyword(s):  
Machine Learning ◽  
Facial Expressions ◽  
Delaunay Triangulation ◽  
Connected Graph ◽  
Recognition Rate ◽  
Machine Learning Techniques ◽  
Expression Recognition ◽  
Technological Advances ◽  
Learning Techniques ◽  
Machine Learning Approach

Facial expressions can tell a lot about an individual’s emotional state. Recent technological advances opening avenues for automatic Facial Expression Recognition (FER) based on machine learning techniques. Many works have been done on FER for the classification of facial expressions. However, the applicability to more naturalistic facial expressions remains unclear. This paper intends to develop a machine learning approach based on the Delaunay triangulation to extract the relevant facial features allowing classifying facial expressions. Initially, from the given facial image, a set of discriminative landmarks are extracted. Along with this, a minimal landmark connected graph is also extracted. Thereby, from the connected graph, the expression is represented by a one-dimensional feature vector. Finally, the obtained vector is subject for classification by six well-known classifiers (KNN, NB, DT, QDA, RF and SVM). The experiments are conducted on four standard databases (CK+, KDEF, JAFFE and MUG) to evaluate the performance of the proposed approach and find out which classifier is better suited to our system. The QDA approach based on the Delaunay triangulation has a high accuracy of 96.94% since it only supports non-zero pixels, which increases the recognition rate.

scholarly journals Novel Descriptors for Effective Recognition of Face and Facial Expressions

2020 ◽  
Vol 34 (5) ◽  
pp. 521-530
Author(s):  
Farid Ayeche ◽  
Adel Alti
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Support Vector Machine Classifier ◽  
Recognition Rate ◽  
Face Image ◽  
Support Vector ◽  
Expression Recognition ◽  
The Face ◽  
Public Datasets ◽  
Edge Response

In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.

scholarly journals Objective Classes for Micro-Facial Expression Recognition

2018 ◽  
Vol 4 (10) ◽  
pp. 119 ◽  
Author(s):  
Adrian Davison ◽  
Walied Merghani ◽  
Moi Yap
Keyword(s):  
Facial Expressions ◽  
Local Binary Patterns ◽  
Expression Recognition ◽  
Coding System ◽  
Histogram Of Oriented Gradient ◽  
Action Units ◽  
Self Reports ◽  
Casme Ii ◽  
Academy Of Sciences ◽  
Micro Expression

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset (Chinese Academy of Sciences Micro-expression II) are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP (Local Binary Patterns from Three Orthogonal Planes), HOOF (Histograms of Oriented Optical Flow) and HOG 3D (3D Histogram of Oriented Gradient) feature descriptors. The experiments are evaluated on two benchmark FACS (Facial Action Coding System) coded datasets: CASME II and SAMM (A Spontaneous Micro-Facial Movement). The best result achieves 86.35% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.

Facial Expression Recognition Using Directional Local Binary Pattern

2014 ◽  
Vol 701-702 ◽  
pp. 395-399
Author(s):  
Ying Tong ◽  
Kun Wang ◽  
Liang Bao Jiao
Keyword(s):  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Gaussian White Noise ◽  
Recognition Rate ◽  
Local Deformation ◽  
Expression Recognition ◽  
Directional Information ◽  
Rate Improvement ◽  
Lbp Descriptor

Local binary pattern (LBP) descriptor could not efficiently describe the gray change in different directions of facial expressions characteristic regions. For this, the directional local binary pattern (DLBP) is put forward to represent facial geometrical characteristic. DLBP encodes the directional information of the face’s facial textures in horizontal, vertical and diagonal three directions, which can effectively describe the characteristic of facial muscles, wrinkles and other local deformation. Experimental results on JAFFE databases demonstrate the algorithm’s effectiveness, where nearly 5 percent recognition rate improvement is obtained beyond traditional LBP. Additional experiments verify robustness and reliability of the proposed DLBP operator within Gaussian white noise and pepper salt noise.

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 Facial Expression Recognition Using Stationary Wavelet Transform Features

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huma Qayyum ◽  
Muhammad Majid ◽  
Syed Muhammad Anwar ◽  
Bilal Khan
Keyword(s):  
Wavelet Transform ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Back Propagation Algorithm ◽  
Stationary Wavelet Transform ◽  
Feed Forward Neural Network ◽  
Current Frequency

Humans use facial expressions to convey personal feelings. Facial expressions need to be automatically recognized to design control and interactive applications. Feature extraction in an accurate manner is one of the key steps in automatic facial expression recognition system. Current frequency domain facial expression recognition systems have not fully utilized the facial elements and muscle movements for recognition. In this paper, stationary wavelet transform is used to extract features for facial expression recognition due to its good localization characteristics, in both spectral and spatial domains. More specifically a combination of horizontal and vertical subbands of stationary wavelet transform is used as these subbands contain muscle movement information for majority of the facial expressions. Feature dimensionality is further reduced by applying discrete cosine transform on these subbands. The selected features are then passed into feed forward neural network that is trained through back propagation algorithm. An average recognition rate of 98.83% and 96.61% is achieved for JAFFE and CK+ dataset, respectively. An accuracy of 94.28% is achieved for MS-Kinect dataset that is locally recorded. It has been observed that the proposed technique is very promising for facial expression recognition when compared to other state-of-the-art techniques.

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