Facial micro-expression recognition: A machine learning approach

Adegun, Iyanu Pelumi; Vadapalli, Hima

doi:10.1016/j.sciaf.2020.e00465

Cited by 30 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, the study was limited to four representative emotions. Although not related to emotion authenticity, Adegun and Vadapalli analyzed microexpressions to recognize seven universal emotions with machine learning [ 34 ].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

The Analysis of Emotion Authenticity Based on Facial Micromovements

Park

Lee

Whang

2021

Sensors

View full text Add to dashboard Cite

People tend to display fake expressions to conceal their true feelings. False expressions are observable by facial micromovements that occur for less than a second. Systems designed to recognize facial expressions (e.g., social robots, recognition systems for the blind, monitoring systems for drivers) may better understand the user’s intent by identifying the authenticity of the expression. The present study investigated the characteristics of real and fake facial expressions of representative emotions (happiness, contentment, anger, and sadness) in a two-dimensional emotion model. Participants viewed a series of visual stimuli designed to induce real or fake emotions and were signaled to produce a facial expression at a set time. From the participant’s expression data, feature variables (i.e., the degree and variance of movement, and vibration level) involving the facial micromovements at the onset of the expression were analyzed. The results indicated significant differences in the feature variables between the real and fake expression conditions. The differences varied according to facial regions as a function of emotions. This study provides appraisal criteria for identifying the authenticity of facial expressions that are applicable to future research and the design of emotion recognition systems.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

The Analysis of Emotion Authenticity Based on Facial Micromovements

Park

Lee

Whang

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…They achieved hits fluctuating between 76. Vadapalli in their facial micro expression recognition system [45], perform a comparison between support vector machines and the Local Binary Pattern (LBP) technique, finding that the latter presents better performance in terms of accuracy, precision and image retrieval, and is also 0.2996 seconds faster in mean training time. That is, it improves the process when using temporal features.…”

Section: C) Support Vector Machines (Svm)mentioning

confidence: 99%

Machine learning models in people detection and identification

2022

View full text Add to dashboard Cite

Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020. Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review? Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people. Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria. Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes. Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes. Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people. Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.

show abstract

“…3 (c). In past micro-expression studies such as [16,[37][38][39], the selection of SVM and KNN as baseline model was motivated by its success. SVM uses a hyperplane to separate data groups into their respective classes.…”

Section: Micro-expression Classificationmentioning

confidence: 99%

A Novel Approach on Motion Estimation for Micro-Expression Recognition Using Phase Only Correlation with All Block Search (POC-ABS)

Rosiani¹,

Choirina²,

Muna³

et al. 2020

IJIES

View full text Add to dashboard Cite

Micro-expression is an expression when a person tries to held or hidden, but the leak of this emotion still occurs in one or two areas of the face or maybe a short expression that across in the whole-face. Not more than 500ms, micro-expressions can be difficult to recognize and detect where the leakage area is located. This study presents a new method to recognize and detect the subtle motion on the facial components area using Phase Only Correlation algorithm with All Block Search (POC-ABS) to estimate the motion of all block areas. This block matching method is proposed by comparing each block in the two frames to determine whether there is movement or not. If the two blocks are identical, then the motion vector value is not displayed, whereas if the blocks are non-identical, the motion vector value of the POC is displayed. The motion vector, which is as a motion feature, estimates whether or not there are movements in the same block. In order to further confirm the reliability of the proposed method, two different classifiers were used for the micro-expression recognition of the CASME II dataset. The highest performance results are for SVM at 94.3 percent and for KNN at 95.6 percent. Finally, this algorithm detects leaks of motion based on the ratio of the motion vectors. The left and right eyebrows are dominant when expressing disgust, sadness, and surprise. Meanwhile, the movements of the right eye and left eye were the most dominant when the happiness expression.

show abstract

Facial micro-expression recognition: A machine learning approach

Cited by 30 publications

References 13 publications

The Analysis of Emotion Authenticity Based on Facial Micromovements

The Analysis of Emotion Authenticity Based on Facial Micromovements

Machine learning models in people detection and identification

A Novel Approach on Motion Estimation for Micro-Expression Recognition Using Phase Only Correlation with All Block Search (POC-ABS)

Contact Info

Product

Resources

About