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Journal articles on the topic 'Binary facial expression recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Tong, Ying, Kun Wang, and Liang Bao Jiao. "Facial Expression Recognition Using Directional Local Binary Pattern." Applied Mechanics and Materials 701-702 (December 2014): 395–99. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.395.

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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

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Xia, Xiao Xiao, Zi Lu Ying, and Wen Jin Chu. "Facial Expression Recognition Based on Monogenic Binary Coding." Applied Mechanics and Materials 511-512 (February 2014): 437–40. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.437.

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A new method based on Monogenic Binary Coding (MBC) is proposed for facial expression feature extraction and representation. Firstly, monogenic signal analysis is used to extract multi-scale magnitude, orientation and phase components. Secondly, Monogenic Binary Coding (MBC) is used to encode the monogenic local variation and intensity in local regions of each extracted component in each scale and local histograms are built. Then Blocked Fisher Linear Discrimination (BFLD) is used to reduce the dimensionality of histogram features and to enhance discrimination. Finally the three complementary

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Feng, X., M. Pietikäinen, and A. Hadid. "Facial expression recognition based on local binary patterns." Pattern Recognition and Image Analysis 17, no. 4 (2007): 592–98. http://dx.doi.org/10.1134/s1054661807040190.

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Owusu, Ebenezer, Jacqueline Asor Kumi, and Justice Kwame Appati. "On Facial Expression Recognition Benchmarks." Applied Computational Intelligence and Soft Computing 2021 (September 17, 2021): 1–20. http://dx.doi.org/10.1155/2021/9917246.

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Facial expression is an important form of nonverbal communication, as it is noted that 55% of what humans communicate is expressed in facial expressions. There are several applications of facial expressions in diverse fields including medicine, security, gaming, and even business enterprises. Thus, currently, automatic facial expression recognition is a hotbed research area that attracts lots of grants and therefore the need to understand the trends very well. This study, as a result, aims to review selected published works in the domain of study and conduct valuable analysis to determine the

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Davison, Adrian, Walied Merghani, and Moi Yap. "Objective Classes for Micro-Facial Expression Recognition." Journal of Imaging 4, no. 10 (2018): 119. http://dx.doi.org/10.3390/jimaging4100119.

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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 Patter

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Hu, De Kun, An Sheng Ye, Li Li, and Li Zhang. "Recognition of Facial Expression via Kernel PCA Network." Applied Mechanics and Materials 631-632 (September 2014): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.498.

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In this work, a kernel principle component analysis network (KPCANet) is proposed for classification of the facial expression in unconstrained images, which comprises only the very basic data processing components: cascaded kernel principal component analysis (KPCA), binary hashing, and block-wise histograms. In the proposed model, KPCA is employed to learn multistage filter banks. It is followed by simple binary hashing and block histograms for indexing and pooling. For comparison and better understanding, We have tested these basic networks extensively on many benchmark visual datasets ( suc

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CAO, NHAN THI, AN HOA TON-THAT, and HYUNG IL CHOI. "FACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PATTERN FEATURES AND SUPPORT VECTOR MACHINE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 06 (2014): 1456012. http://dx.doi.org/10.1142/s0218001414560126.

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Facial expression recognition has been researched much in recent years because of their applications in intelligent communication systems. Many methods have been developed based on extracting Local Binary Pattern (LBP) features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expressions based on Local Binary Pattern features and Support Vector Machine with two effective improvements. First is the preprocessing step and second is the method of dividing face

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Wu, Zhaoqi, Reziwanguli Xiamixiding, Atul Sajjanhar, Juan Chen, and Quan Wen. "Image Appearance-Based Facial Expression Recognition." International Journal of Image and Graphics 18, no. 02 (2018): 1850012. http://dx.doi.org/10.1142/s0219467818500122.

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We investigate facial expression recognition (FER) based on image appearance. FER is performed using state-of-the-art classification approaches. Different approaches to preprocess face images are investigated. First, region-of-interest (ROI) images are obtained by extracting the facial ROI from raw images. FER of ROI images is used as the benchmark and compared with the FER of difference images. Difference images are obtained by computing the difference between the ROI images of neutral and peak facial expressions. FER is also evaluated for images which are obtained by applying the Local binar

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V.Jonnalagedda, Megha, and Dharmpal D. Doye. "Radially Defined Local Binary Patterns for Facial Expression Recognition." International Journal of Computer Applications 119, no. 21 (2015): 17–22. http://dx.doi.org/10.5120/21360-4369.

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Huang, Xiaohua, Guoying Zhao, Wenming Zheng, and Matti Pietikainen. "Spatiotemporal Local Monogenic Binary Patterns for Facial Expression Recognition." IEEE Signal Processing Letters 19, no. 5 (2012): 243–46. http://dx.doi.org/10.1109/lsp.2012.2188890.

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Moore, S., and R. Bowden. "Local binary patterns for multi-view facial expression recognition." Computer Vision and Image Understanding 115, no. 4 (2011): 541–58. http://dx.doi.org/10.1016/j.cviu.2010.12.001.

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Sultan Zia, M., Majid Hussain, and M. Arfan Jaffar. "Incremental Learning-Based Facial Expression Classification System Using a Novel Multinomial Classifier." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 04 (2017): 1856004. http://dx.doi.org/10.1142/s0218001418560049.

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Facial expressions recognition is a crucial task in pattern recognition and it becomes even crucial when cross-cultural emotions are encountered. Various studies in the past have shown that all the facial expressions are not innate and universal, but many of them are learned and culture-dependent. Extreme facial expression recognition methods employ different datasets for training and later use it for testing and demostrate high accuracy in recognition. Their performances degrade drastically when expression images are taken from different cultures. Moreover, there are many existing facial expr

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Hassan, S. M., A. Alghamdi, A. Hafeez, M. Hamdi, I. Hussain, and M. Alrizq. "An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition." Engineering, Technology & Applied Science Research 11, no. 3 (2021): 7172–76. http://dx.doi.org/10.48084/etasr.4080.

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In order to explore the accompanying examination goals for facial expression recognition, a proper combination of classification and adequate feature extraction is necessary. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In this paper, a new fusion technique for human facial expression recognition is used to accurately recognize human facial expressions. A combination of Discrete Wavelet Features (DWT), Local Binary Pattern (LBP), and Histogram of Gradients (HoG) feature extraction techniques was used to investigate six human emotions. K-

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Liu, Zhen-Tao, Si-Han Li, Wei-Hua Cao, Dan-Yun Li, Man Hao, and Ri Zhang. "Combining 2D Gabor and Local Binary Pattern for Facial Expression Recognition Using Extreme Learning Machine." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (2019): 444–55. http://dx.doi.org/10.20965/jaciii.2019.p0444.

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The efficiency of facial expression recognition (FER) is important for human-robot interaction. Detection of the facial region, extraction of discriminative facial expression features, and identification of categories of facial expressions are all related to the recognition accuracy and time-efficiency. An FER framework is proposed, in which 2D Gabor and local binary pattern (LBP) are combined to extract discriminative features of salient facial expression patches, and extreme learning machine (ELM) is adopted to identify facial expression categories. The combination of 2D Gabor and LBP can no

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Snehitha, Kanaparthi. "Facial Expression Recognition with Appearance Based Features of Facial Landmarks." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3343–48. http://dx.doi.org/10.22214/ijraset.2021.35702.

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Artificial intelligence technology has been trying to bridge the gap between humans and machines. The latest development in this technology is Facial recognition. Facial recognition technology identifies the faces by co-relating and verifying the patterns of facial contours. Facial recognition is done by using Viola-Jones object detection framework. Facial expression is one of the important aspects in recognizing human emotions. Facial expression also helps to determine interpersonal relation between humans. Automatic facial recognition is now being used very widely in almost every field, like

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Cen, Shixin, Yang Yu, Gang Yan, Ming Yu, and Qing Yang. "Sparse Spatiotemporal Descriptor for Micro-Expression Recognition Using Enhanced Local Cube Binary Pattern." Sensors 20, no. 16 (2020): 4437. http://dx.doi.org/10.3390/s20164437.

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As a spontaneous facial expression, a micro-expression can reveal the psychological responses of human beings. Thus, micro-expression recognition can be widely studied and applied for its potentiality in clinical diagnosis, psychological research, and security. However, micro-expression recognition is a formidable challenge due to the short-lived time frame and low-intensity of the facial actions. In this paper, a sparse spatiotemporal descriptor for micro-expression recognition is developed by using the Enhanced Local Cube Binary Pattern (Enhanced LCBP). The proposed Enhanced LCBP is composed

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Jasuja, Arush, and Sonia Rathee. "Emotion Recognition Using Facial Expressions." International Journal of Information Retrieval Research 11, no. 3 (2021): 1–17. http://dx.doi.org/10.4018/ijirr.2021070101.

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Emotion recognition is an important aspect of human interaction, and this ability of humans to interpret emotions based on facial expressions is a basic element for effective communication. Machine learning can help automate this complicated task with the help of feature engineering. This work proposes some pipelines trained on the JAFFE dataset using feature extraction methods, namely principal component analysis (PCA) and local binary pattern (LBP) combined with Fisher discriminant ratio (FDR) as a feature selection method. In order to build a classification scheme capable of successfully id

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H S, Gunavathi, and Siddappa M. "Robust hybrid framework for automatic facial expression recognition." International Journal of Engineering & Technology 7, no. 2 (2018): 568. http://dx.doi.org/10.14419/ijet.v7i2.10764.

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Over the last few years, facial expression recognition is an active research field, which has an extensive range of applications in the area of social interaction, social intelligence, autism detection and Human-computer interaction. In this paper, a robust hybrid framework is presented to recognize the facial expressions, which enhances the efficiency and speed of recognition system by extracting significant features of a face. In the proposed framework, feature representation and extraction are done by using Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). Later, the di

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Zhang, Zheng, and Chao Xu. "A Noval Distributed Architecture for Expression Recognition." Advanced Materials Research 403-408 (November 2011): 3199–202. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3199.

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A distributed facial expression recognition approach based on MB-LGBP feature and decision fusion is presented in this paper to accomplish subject-independent facial expression recognition more efficiently. At first, the Multi-scale Block Local Gabor Binary Patterns (MB-LGBP) are extracted from expression regions to achieve both locally and globally informative features. Then a distributed architecture is proposed to accelerate the recognition process, in which features of each single region are utilized to perform expression classification in parallel. The final decision is made by an artific

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20

Kasim, Shahreen, Rohayanti Hassan, Nur Hadiana Zaini, Asraful Syifaa’ Ahmad, Azizul Azhar Ramli, and Rd Rohmat Saedudin. "A Study on Facial Expression Recognition Using Local Binary Pattern." International Journal on Advanced Science, Engineering and Information Technology 7, no. 5 (2017): 1621. http://dx.doi.org/10.18517/ijaseit.7.5.3390.

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AYENI, OLANIYI ABIODUN. "RECOGNITION OF FACIAL EXPRESSION BASED ON LOCAL BINARY PATTERNS (LBP)." i-manager's Journal on Computer Science 7, no. 3 (2019): 14. http://dx.doi.org/10.26634/jcom.7.3.16861.

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ASHIR, Abubakar M., and Alaa ELEYAN. "A MONOGENIC LOCAL GABOR BINARY PATTERN FOR FACIAL EXPRESSION RECOGNITION." Selcuk University Journal of Engineering ,Science and Technology 5, no. 4 (2017): 414–22. http://dx.doi.org/10.15317/scitech.2017.101.

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23

Yang, Hong, and Deli Zhu. "A Facial Expression Recognition Algorithm Based on Local Binary Pattern and Empirical Mode Decomposition." Open Electrical & Electronic Engineering Journal 8, no. 1 (2014): 599–604. http://dx.doi.org/10.2174/1874129001408010599.

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To improve the efficiency of facial expression recognition, this paper puts forward a kind of recognition algorithm based on local binary pattern (LBP) and empirical mode decomposition (EMD). First of all, process the empirical mode decomposition into the preprocessing facial image, and bring forward many a high frequency images instead of the original image; then, divide the sub domain of the high frequency image and obtain the sub domain LBP histogram and full face histogram; finally, identify the expression of the generated LBP histogram. Through the experiment on JAFFE database, it shows t

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Zheng, Wei Hao, Wei Wang, and Yi De Ma. "Facial Expression Recognition Based on the Texture Features of Global Principal Component and Local Boundary." Applied Mechanics and Materials 548-549 (April 2014): 1110–17. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1110.

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Facial expression recognition is a key ingredient to either emotion analysis or pattern recognition, which is also an important component in human-machine interaction. In facial expression analysis, one of the well-known methods to obtain the texture of expressions is local binary patterns (LBP) which compares pixels in local region and encodes the comparison result in forms of histogram. However, we argue that the textures of expressions are not accurate and still contain some irrelevant information, especially in the region between eyes and mouth. In this paper, we propose a compound method

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Li, Li Sai, Zi Lu Ying, and Bin Bin Huang. "Facial Expression Recognition Based on Gabor Texture Features and Centre Binary Pattern." Applied Mechanics and Materials 742 (March 2015): 257–60. http://dx.doi.org/10.4028/www.scientific.net/amm.742.257.

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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-ba

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Kumar, Yogesh, Shashi Kant Verma, and Sandeep Sharma. "Quantum-inspired binary gravitational search algorithm to recognize the facial expressions." International Journal of Modern Physics C 31, no. 10 (2020): 2050138. http://dx.doi.org/10.1142/s0129183120501387.

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This paper addresses an autonomous facial expression recognition system using the feature selection approach of the Quantum-Inspired Binary Gravitational Search Algorithm (QIBGSA). The detection of facial features completely depends upon the selection of precise features. The concept of QIBGSA is a modified binary version of the gravitational search algorithm by mimicking the properties of quantum mechanics. The QIBGSA approach reduces the computation cost for the initial extracted feature set using the hybrid approach of Local binary patterns with Gabor filter method. The proposed automated s

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Wang, Yan, Ming Li, Xing Wan, Congxuan Zhang, and Yue Wang. "Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition." Computational Intelligence and Neuroscience 2020 (December 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/8886872.

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Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field. In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition. First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale Block Local Binary Pattern Uniform Histogram (MB-LBPUH) descriptor filtering over the training samples. According to the parameters, we build various fusion feature spaces by employing multiclass l

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Prajapat, Gopal Krishan, and Rakesh Kumar. "A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis." International Journal of Electronics, Communications, and Measurement Engineering 8, no. 2 (2019): 1–25. http://dx.doi.org/10.4018/ijecme.2019070101.

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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 bin

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Zhao, Lei, Zengcai Wang, and Guoxin Zhang. "Facial Expression Recognition from Video Sequences Based on Spatial-Temporal Motion Local Binary Pattern and Gabor Multiorientation Fusion Histogram." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7206041.

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This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in-plane head rotation and break up facial region from background. Then, spatial-temporal motion local binary pattern (LBP) feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information of facial expressions. Finally, a one-versus-one strategy bas

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TCHANGOU TOUDJEU, I., and J. R. TAPAMO. "Circular Derivative Local Binary Pattern Feature Description for Facial Expression Recognition." Advances in Electrical and Computer Engineering 19, no. 1 (2019): 51–56. http://dx.doi.org/10.4316/aece.2019.01007.

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31

Won, Chulho. "A Study on Facial Expression Recognition using Boosted Local Binary Pattern." Journal of Korea Multimedia Society 16, no. 12 (2013): 1357–67. http://dx.doi.org/10.9717/kmms.2013.16.12.1357.

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Arshid, Sadia, Ayyaz Hussain, Asim Munir, Anum Nawaz, and Sanneya Aziz. "Multi-stage binary patterns for facial expression recognition in real world." Cluster Computing 21, no. 1 (2017): 323–31. http://dx.doi.org/10.1007/s10586-017-0832-5.

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Shan, Caifeng, Shaogang Gong, and Peter W. McOwan. "Facial expression recognition based on Local Binary Patterns: A comprehensive study." Image and Vision Computing 27, no. 6 (2009): 803–16. http://dx.doi.org/10.1016/j.imavis.2008.08.005.

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Jeyalaksshmi, S., and S. Prasanna. "Simultaneous evolutionary neural network based automated video based facial expression analysis." International Journal of Engineering & Technology 7, no. 1.1 (2017): 125. http://dx.doi.org/10.14419/ijet.v7i1.1.9211.

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In real life scenario, facial expressions and emotions are nothing but responses to the external and internal events of human being. In Human Computer Interaction (HCI), recognition of end user’s expressions and emotions from the video streaming plays very important role. In such systems it is required to track the dynamic changes in human face movements quickly in order to deliver the required response system. In real time applications, this Facial Expression Recognition (FER) is very helpful like physical fatigue detection based on facial detection and expressions such as driver fatigue dete

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Niu, Ben, Zhenxing Gao, and Bingbing Guo. "Facial Expression Recognition with LBP and ORB Features." Computational Intelligence and Neuroscience 2021 (January 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/8828245.

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Emotion plays an important role in communication. For human–computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications used in real-life conditions, to gain better results without DNNs, in this paper, we propose an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and

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Wang, Fowei, Bo Shen, Shaoyuan Sun, and Zidong Wang. "Improved GA and Pareto optimization-based facial expression recognition." Assembly Automation 36, no. 2 (2016): 192–99. http://dx.doi.org/10.1108/aa-11-2015-110.

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Purpose The purpose of this paper is to improve the accuracy of the facial expression recognition by using genetic algorithm (GA) with an appropriate fitness evaluation function and Pareto optimization model with two new objective functions. Design/methodology/approach To achieve facial expression recognition with high accuracy, the Haar-like features representation approach and the bilateral filter are first used to preprocess the facial image. Second, the uniform local Gabor binary patterns are used to extract the facial feature so as to reduce the feature dimension. Third, an improved GA an

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Chen, Xiangmin, Li Ke, Qiang Du, Jinghui Li, and Xiaodi Ding. "Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM." Complexity 2021 (January 4, 2021): 1–12. http://dx.doi.org/10.1155/2021/6616158.

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Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL) model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vector machine (DAGSVM). We

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Uma Maheswari, V., Vara Prasad, and S. Viswanadha Raju. "Local Directional Threshold based Binary Patterns for Facial Expression Recognition and Analysis." International Journal of Engineering & Technology 7, no. 4.6 (2018): 17. http://dx.doi.org/10.14419/ijet.v7i4.6.20225.

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In this paper, proposing a novel method to retrieve the edge and texture information from facial images named local directional standard matrix (LDSM) and local dynamic threshold based binary pattern (LDTBP). LBP and LTP operators are used for texture extraction of an image by finding difference between center and surrounding pixels but they failed to detect edges and large intensity variations. Thus addressed such problems in proposed method firstly, calculated the LDSM matrix with standard deviation of horizontal and vertical pixels of each pixel. Therefore, values are encoded based on the d

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Huang, Lei, Fei Xie, Jing Zhao, Shibin Shen, Weiran Guang, and Rongjian Lu. "Human Emotion Recognition Based on Face and Facial Expression Detection Using Deep Belief Network Under Complicated Backgrounds." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 14 (2020): 2056010. http://dx.doi.org/10.1142/s0218001420560108.

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The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Se

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Li, Zhi Jie, Xiao Dong Duan, and Cun Rui Wang. "Automatic Expression Recognition Based on Mouth Shape Analysis." Applied Mechanics and Materials 644-650 (September 2014): 4018–22. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4018.

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This paper presents a simple approach for facial expression recognition. In the preprocessing stage, rough lips region is obtained from original face image using HSI space. Then, based on the binary image, exact lips region is located within a rectangle. To achieve this goal, PSO algorithm is applied to search for the best rectangle region. Finally, expression is estimated based on the parts ratio of lips. The simulation results show that this geometric approach is accurate and effective, even for the slightly smile.

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Zhao, Yue, and Jiancheng Xu. "An Improved Micro-Expression Recognition Method Based on Necessary Morphological Patches." Symmetry 11, no. 4 (2019): 497. http://dx.doi.org/10.3390/sym11040497.

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Micro-expression is a spontaneous emotional representation that is not controlled by logic. A micro-expression is both transitory (short duration) and subtle (small intensity), so it is difficult to detect in people. Micro-expression detection is widely used in the fields of psychological analysis, criminal justice and human-computer interaction. Additionally, like traditional facial expressions, micro-expressions also have local muscle movement. Psychologists have shown micro-expressions have necessary morphological patches (NMPs), which are triggered by emotion. Furthermore, the objective of

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Zhao, Xiaoming, and Shiqing Zhang. "Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap." Sensors 11, no. 10 (2011): 9573–88. http://dx.doi.org/10.3390/s111009573.

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Agarwal, Rashi, Mayur Rahul, and Narendra Kohli. "Facial Expression Recognition using Local Binary Pattern and Modified Hidden Markov Model." International Journal of Advanced Intelligence Paradigms 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijaip.2018.10020898.

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Rahul, Mayur, Narendra Kohli, and Rashi Agarwal. "Facial expression recognition using local binary pattern and modified hidden Markov model." International Journal of Advanced Intelligence Paradigms 17, no. 3/4 (2020): 367. http://dx.doi.org/10.1504/ijaip.2020.109523.

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Lu, Hua. "Divided Local Binary Pattern (DLBP) Features Description Method For Facial Expression Recognition." Journal of Information and Computational Science 11, no. 7 (2014): 2425–33. http://dx.doi.org/10.12733/jics20103426.

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Wang, Yandan, John See, Raphael C. W. Phan, and Yee-Hui Oh. "Efficient Spatio-Temporal Local Binary Patterns for Spontaneous Facial Micro-Expression Recognition." PLOS ONE 10, no. 5 (2015): e0124674. http://dx.doi.org/10.1371/journal.pone.0124674.

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Gogić, Ivan, Martina Manhart, Igor S. Pandžić, and Jörgen Ahlberg. "Fast facial expression recognition using local binary features and shallow neural networks." Visual Computer 36, no. 1 (2018): 97–112. http://dx.doi.org/10.1007/s00371-018-1585-8.

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Zhang, Yan, and Caijian Hua. "Driver fatigue recognition based on facial expression analysis using local binary patterns." Optik 126, no. 23 (2015): 4501–5. http://dx.doi.org/10.1016/j.ijleo.2015.08.185.

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Cai, Jun, Yang Yang Li, and Yi Zhang. "Mixed Expression Recognition & Analysis Based on Compressed Sense and Subjection Degree." Applied Mechanics and Materials 548-549 (April 2014): 1118–23. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1118.

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Abstract:

Mixed expression is more in line with people’s daily perfromance than basic expression.This paper proposed a facial expression recognition method that recognizes and analyze mixed expressions. In this method, Gabor phase and local binary patterns were combined into GPLBP model to obtain the expression features and the model contained good robustness of light. Compressed sense and subjection degree function were adopted to identify the ingredients of main basic expressions in the mixed expression the ratio of each kind of basic expression. Experimental results of comparison respectively to Gabo

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Goyani, Mahesh M., and Narendra Patel. "Recognition of Facial Expressions using Local Mean Binary Pattern." ELCVIA Electronic Letters on Computer Vision and Image Analysis 16, no. 1 (2017): 54. http://dx.doi.org/10.5565/rev/elcvia.1058.

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