scholarly journals An Improved Micro-Expression Recognition Method Based on Necessary Morphological Patches

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 497 ◽  
Author(s):  
Yue Zhao ◽  
Jiancheng Xu
Keyword(s):  
Optical Flow ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Optical Flow Method ◽  
Psychological Analysis ◽  
Emotional Representation ◽  
Discriminant Ability ◽  
Casme Ii ◽  
Micro Expression

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 this paper is to sort and filter these NMPs and extract features from NMPs to train classifiers to recognize micro-expressions. Firstly, we use the optical flow method to compare the on-set frame and the apex frame of the micro-expression sequences. By doing this, we could find facial active patches. Secondly, to find the NMPs of micro-expressions, this study calculates the local binary pattern from three orthogonal planes (LBP-TOP) operators and cascades them with optical flow histograms to form the fusion features of the active patches. Finally, a random forest feature selection (RFFS) algorithm is used to identify the NMPs and to characterize them via support vector machine (SVM) classifier. We evaluated the proposed method on two popular publicly available databases: CASME II and SMIC. Results show that NMPs are statistically determined and contribute to significant discriminant ability instead of holistic utilization of all facial regions.

Deep Convolutional Neural Network with Optical Flow for Facial Micro-Expression Recognition

2019 ◽  
Vol 29 (01) ◽  
pp. 2050006 ◽  
Author(s):  
Qiuyu Li ◽  
Jun Yu ◽  
Toru Kurihara ◽  
Haiyan Zhang ◽  
Shu Zhan
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Video Clip ◽  
Support Vector ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Landmarks ◽  
Flow Features ◽  
Optical Flow Features ◽  
Micro Expression

Micro-expression is a kind of brief facial movements which could not be controlled by the nervous system. Micro-expression indicates that a person is hiding his true emotion consciously. Micro-expression recognition has various potential applications in public security and clinical medicine. Researches are focused on the automatic micro-expression recognition, because it is hard to recognize the micro-expression by people themselves. This research proposed a novel algorithm for automatic micro-expression recognition which combined a deep multi-task convolutional network for detecting the facial landmarks and a fused deep convolutional network for estimating the optical flow features of the micro-expression. First, the deep multi-task convolutional network is employed to detect facial landmarks with the manifold-related tasks for dividing the facial region. Furthermore, a fused convolutional network is applied for extracting the optical flow features from the facial regions which contain the muscle changes when the micro-expression appears. Because each video clip has many frames, the original optical flow features of the whole video clip will have high number of dimensions and redundant information. This research revises the optical flow features for reducing the redundant dimensions. Finally, a revised optical flow feature is applied for refining the information of the features and a support vector machine classifier is adopted for recognizing the micro-expression. The main contribution of work is combining the deep multi-task learning neural network and the fusion optical flow network for micro-expression recognition and revising the optical flow features for reducing the redundant dimensions. The results of experiments on two spontaneous micro-expression databases prove that our method achieved competitive performance in micro-expression recognition.

scholarly journals Multi-scale joint feature network for micro-expression recognition

2021 ◽  
Author(s):  
Xinyu Li ◽  
Guangshun Wei ◽  
Jie Wang ◽  
Yuanfeng Zhou
Keyword(s):  
Optical Flow ◽  
Expression Recognition ◽  
Facial Motion ◽  
Multi Scale ◽  
Flow Image ◽  
Wide Range ◽  
Recognition Ability ◽  
Benchmark Datasets ◽  
Casme Ii ◽  
Micro Expression

AbstractMicro-expression recognition is a substantive cross-study of psychology and computer science, and it has a wide range of applications (e.g., psychological and clinical diagnosis, emotional analysis, criminal investigation, etc.). However, the subtle and diverse changes in facial muscles make it difficult for existing methods to extract effective features, which limits the improvement of micro-expression recognition accuracy. Therefore, we propose a multi-scale joint feature network based on optical flow images for micro-expression recognition. First, we generate an optical flow image that reflects subtle facial motion information. The optical flow image is then fed into the multi-scale joint network for feature extraction and classification. The proposed joint feature module (JFM) integrates features from different layers, which is beneficial for the capture of micro-expression features with different amplitudes. To improve the recognition ability of the model, we also adopt a strategy for fusing the feature prediction results of the three JFMs with the backbone network. Our experimental results show that our method is superior to state-of-the-art methods on three benchmark datasets (SMIC, CASME II, and SAMM) and a combined dataset (3DB).

scholarly journals A Convolutional Neural Network for Compound Micro-Expression Recognition

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5553
Author(s):  
Yue Zhao ◽  
Jiancheng Xu
Keyword(s):  
Optical Flow ◽  
Mental States ◽  
Expression Recognition ◽  
Human Beings ◽  
Synthetic Compound ◽  
Flow Maps ◽  
Flow Information ◽  
Casme Ii ◽  
High Level ◽  
Micro Expression

Human beings are particularly inclined to express real emotions through micro-expressions with subtle amplitude and short duration. Though people regularly recognize many distinct emotions, for the most part, research studies have been limited to six basic categories: happiness, surprise, sadness, anger, fear, and disgust. Like normal expressions (i.e., macro-expressions), most current research into micro-expression recognition focuses on these six basic emotions. This paper describes an important group of micro-expressions, which we call compound emotion categories. Compound micro-expressions are constructed by combining two basic micro-expressions but reflect more complex mental states and more abundant human facial emotions. In this study, we firstly synthesized a Compound Micro-expression Database (CMED) based on existing spontaneous micro-expression datasets. These subtle feature of micro-expression makes it difficult to observe its motion track and characteristics. Consequently, there are many challenges and limitations to synthetic compound micro-expression images. The proposed method firstly implemented Eulerian Video Magnification (EVM) method to enhance facial motion features of basic micro-expressions for generating compound images. The consistent and differential facial muscle articulations (typically referred to as action units) associated with each emotion category have been labeled to become the foundation of generating compound micro-expression. Secondly, we extracted the apex frames of CMED by 3D Fast Fourier Transform (3D-FFT). Moreover, the proposed method calculated the optical flow information between the onset frame and apex frame to produce an optical flow feature map. Finally, we designed a shallow network to extract high-level features of these optical flow maps. In this study, we synthesized four existing databases of spontaneous micro-expressions (CASME I, CASME II, CAS(ME)2, SAMM) to generate the CMED and test the validity of our network. Therefore, the deep network framework designed in this study can well recognize the emotional information of basic micro-expressions and compound micro-expressions.

scholarly journals Classification of Action Based Video using Heterogeneous Feature Extraction and SVM

2019 ◽  
Vol 8 (11) ◽  
pp. 1887-1892
Keyword(s):  
Optical Flow ◽  
Video Sequence ◽  
Human Action ◽  
Video Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Video Frames ◽  
Integral Role ◽  
Heterogeneous Feature

Action recognition (AR) plays a fundamental role in computer vision and video analysis. We are witnessing an astronomical increase of video data on the web and it is difficult to recognize the action in video due to different view point of camera. For AR in video sequence, it depends upon appearance in frame and optical flow in frames of video. In video spatial and temporal components of video frames features play integral role for better classification of action in videos. In the proposed system, RGB frames and optical flow frames are used for AR with the help of Convolutional Neural Network (CNN) pre-trained model Alex-Net extract features from fc7 layer. Support vector machine (SVM) classifier is used for the classification of AR in videos. For classification purpose, HMDB51 dataset have been used which includes 51 Classes of human action. The dataset is divided into 51 action categories. Using SVM classifier, extracted features are used for classification and achieved best result 95.6% accuracy as compared to other techniques of the state-of- art.v

A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow

2018 ◽  
Vol 06 (04) ◽  
pp. 267-275
Author(s):  
Ajay Shankar ◽  
Mayank Vatsa ◽  
P. B. Sujit
Keyword(s):  
Optical Flow ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Support Vector ◽  
Svm Classifier ◽  
Range Finding ◽  
Image Patches ◽  
Computational Resources

Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and payload capacity. Further, existing direct range-finding methods have the trade-off of complexity against range. In this paper, we propose a vision-based system for obstacle detection which is lightweight and useful for low-cost robots. Currently, monocular vision approaches used in the literature suffer from various environmental constraints such as texture and color. To mitigate these limitations, a novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles. A support vector machine (SVM) classifier that uses [Formula: see text]-HOOF for real-time obstacle classification is utilized. To avoid obstacles, a behavior-based collision avoidance mechanism is designed that updates the probability of encountering an obstacle while navigating. The proposed approach depends only on the relative motion of the robot with respect to its surroundings, and therefore is suitable for both indoor and outdoor applications and has been validated through simulated and hardware experiments.

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.

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