Effect of Short-Term Micro-expression Training on the Micro-expression Recognition Performance of Preschool Children

2019 ◽  
pp. 54-62
Author(s):  
Fangbing Qu ◽  
Supei Yan ◽  
Jiuqing Liang ◽  
Jianping Wang
Keyword(s):  
Preschool Children ◽  
Recognition Performance ◽  
Expression Recognition ◽  
Short Term ◽  
Micro Expression

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.

Filter selection for image processing before the landmark detection stage for micro-expression analysis

2021 ◽  
Author(s):  
O.V. Melnik ◽  
V.A. Sablina ◽  
G. Burresi ◽  
A.V. Savin
Keyword(s):  
Image Processing ◽  
Expression Analysis ◽  
Recognition Performance ◽  
Emotional Reactions ◽  
Video Image ◽  
Expression Recognition ◽  
Landmark Detection ◽  
Analysis Pipeline ◽  
Micro Expression ◽  
Video Image Processing

The automated estimation of the psycho-emotional state of the human and their emotional reactions to the different influences using the video image analysis is an urgent task in different fields, such as: safeguarding in manufacturing, aviation, transportation, prevention of the crimes and terroristic threats, marketing researches etc. A promising direction is the facial micro-expression analysis. The facial micro-expressions are not under conscious control and reflect the objective emotional reaction. One of the key stages of the procedure of the automatic emotion estimation by the facial micro-expressions is the correct facial landmark detection. It is a complex task because of the presence of the different noise in the consecutive frames. Purpose – the investigation of the ways of increasing the performance of the facial micro-expression analysis pipeline by using preliminary video image processing procedures. It is shown that, as the preliminary stage of the micro-expression analysis pipeline, it is reasonable to perform the blurring of the original images to obtain the more stable results. The determined filtering parameters provide the MediaPipe framework a performance increase for the micro-expression analysis problems. It is shown that the video image blurring by the Gaussian filter with a size of 15×15 pixels allows to reduce the noise influence and to decrease the incorrect shifts of the facial landmarks from frame to frame induced by this noise. The proposed procedure of preliminary video image processing can be used for increasing the facial micro-expression recognition performance in emotion recognition systems based on the video sequence analysis.

Composite Binary Pattern Assisted Micro-Expressions Spotting Through Feature Difference Analysis

2021 ◽  
Vol 23 (07) ◽  
pp. 489-501
Author(s):  
Sammaiah Seelothu ◽  
◽  
Dr. K. Venugopal Rao ◽  
Keyword(s):  
Recognition Performance ◽  
Expression Recognition ◽  
Texture Descriptor ◽  
Facial Movement ◽  
High Stakes ◽  
Computational Burden ◽  
Difference Analysis ◽  
Feature Difference ◽  
Micro Expression

Micro-Expressions (MEs) are one kind of facial movement which is very spontaneous and involuntary in nature. MEs are observed when a person attempts to hide or conceal the experiencing emotion in a high-stakes environment. The duration of ME is very short and approximately less than 500 milliseconds. Recognition of such kinds of expressions from lengthy video consequences to a limited Micro Expression Recognition Performance and also creates the computational burden. Hence, in this paper, we propose a new ME spotting (detection of ME frames) method based on a new texture descriptor called Composite Binary Pattern (CBP). As a pre-processing, we employ the viola jones algorithm for landmark regions detection followed by landmark points detection for facial alignment. Next, every aligned face is described through CBP and subjected to feature difference analysis followed by the threshold for ME spotting. For simulation, the REVIEW dataset is used and the performance is measured through Recall, Precision, and F-Score.

scholarly journals Motion and Geometric Feature Analysis for Real-time Automatic Micro-expression Recognition Systems

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1029
Author(s):  
Adamu Muhammad Buhari ◽  
Chee-Pun Ooi ◽  
Vishnu Monn Baskaran ◽  
Wooi-Haw Tan
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Recognition Performance ◽  
Detection Algorithm ◽  
Geometric Feature ◽  
Expression Recognition ◽  
Image Size ◽  
Recognition Systems ◽  
Full Face ◽  
Micro Expression

The trend of real-time micro-expression recognition systems has increased with recent advancements in human-computer interaction (HCI) in security and healthcare. Several studies in this field contributed towards recognition accuracy, while few studies look into addressing the computation costs. In this paper, two approaches for micro-expression feature extraction are analyzed for real-time automatic micro-expression recognition. Firstly, motion-based approach, which calculates motion of subtle changes from an image sequence and present as features. Then, secondly, a low computational geometric-based feature extraction technique, a very popular method for facial expression recognition in real-time. These approaches were integrated in a developed system together with a facial landmark detection algorithm and a classifier for real-time analysis. Moreover, the recognition performance were evaluated using SMIC, CASME, CAS(ME)2 and SAMM datasets. The results suggest that the optimized Bi-WOOF (leveraging on motion-based features) yields the highest accuracy of 68.5%, while the full-face graph (leveraging on geometric-based features) yields 75.53% on the SAMM dataset. On the other hand, the optimized Bi-WOOF processes sample at 0.36 seconds and full-face graph processes sample at 0.10 seconds with a 640x480 image size. All experiments were performed on an Intel i5-3470 machine.

Micro-Expression Recognition using 3D - CNN

2020 ◽  
pp. 5-13
Author(s):  
Vishal Dubey ◽  
◽  
◽  
◽  
Bhavya Takkar ◽  
...  
Keyword(s):  
Emotional Response ◽  
High Speed ◽  
Actual State ◽  
Time Interval ◽  
Short Time Interval ◽  
Expression Recognition ◽  
Specific Emotion ◽  
3D Cnn ◽  
Short Time ◽  
Micro Expression

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.

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