Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction

2018 ◽  
pp. 652-664 ◽  
Author(s):  
Madhumita A. Takalkar ◽  
Haimin Zhang ◽  
Min Xu
Keyword(s):  
Feature Extraction ◽  
Recognition Accuracy ◽  
Expression Recognition ◽  
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.

scholarly journals Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition

2020 ◽  
Vol 11 (4) ◽  
pp. 25-37
Author(s):  
Trang Thanh Quynh Le ◽  
Thuong-Khanh Tran ◽  
Manjeet Rege
Keyword(s):  
Feature Extraction ◽  
Computation Method ◽  
Expression Recognition ◽  
Practical Applications ◽  
Convolutional Networks ◽  
Feature Representations ◽  
Low Intensity ◽  
Feature Descriptors ◽  
Image Computation ◽  
Micro Expression

Facial micro-expression is a subtle and involuntary facial expression that exhibits short duration and low intensity where hidden feelings can be disclosed. The field of micro-expression analysis has been receiving substantial awareness due to its potential values in a wide variety of practical applications. A number of studies have proposed sophisticated hand-crafted feature representations in order to leverage the task of automatic micro-expression recognition. This paper employs a dynamic image computation method for feature extraction so that features can be learned on certain localized facial regions along with deep convolutional networks to identify micro-expressions presented in the extracted dynamic images. The proposed framework is simple as opposed to other existing frameworks which used complex hand-crafted feature descriptors. For performance evaluation, the framework is tested on three publicly available databases, as well as on the integrated database in which individual databases are merged into a data pool. Impressive results from the series of experimental work show that the technique is promising in recognizing micro-expressions.

scholarly journals FACS-Based Graph Features for Real-Time Micro-Expression Recognition

2020 ◽  
Vol 6 (12) ◽  
pp. 130
Author(s):  
Adamu Muhammad Buhari ◽  
Chee-Pun Ooi ◽  
Vishnu Monn Baskaran ◽  
Raphaël C. W. Phan ◽  
KokSheik Wong ◽  
...  
Keyword(s):  
Real Time ◽  
Recognition Accuracy ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Landmark ◽  
Single Input ◽  
Measured Distance ◽  
The Cost ◽  
Micro Expression ◽  
Real Time Application

Several studies on micro-expression recognition have contributed mainly to accuracy improvement. However, the computational complexity receives lesser attention comparatively and therefore increases the cost of micro-expression recognition for real-time application. In addition, majority of the existing approaches required at least two frames (i.e., onset and apex frames) to compute features of every sample. This paper puts forward new facial graph features based on 68-point landmarks using Facial Action Coding System (FACS). The proposed feature extraction technique (FACS-based graph features) utilizes facial landmark points to compute graph for different Action Units (AUs), where the measured distance and gradient of every segment within an AU graph is presented as feature. Moreover, the proposed technique processes ME recognition based on single input frame sample. Results indicate that the proposed FACS-baed graph features achieve up to 87.33% of recognition accuracy with F1-score of 0.87 using leave one subject out cross-validation on SAMM datasets. Besides, the proposed technique computes features at the speed of 2 ms per sample on Xeon Processor E5-2650 machine.

scholarly journals Face Expression Recognition using Convolution Neural Network (CNN) Models

2020 ◽  
Vol 11 (4) ◽  
pp. 1-11
Author(s):  
Nahla Nour ◽  
Mohammed Elhebir ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Face Expression ◽  
Result Show ◽  
Face Expression Recognition

This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using three model. In this work, a simple solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that AlexNet model achieved the best accuracy (88.2%) compared to other models.

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.

scholarly journals Elderly Depression Recognition Based on Facial Micro-Expression Extraction

2021 ◽  
Vol 38 (4) ◽  
pp. 1123-1130
Author(s):  
Wei Huang
Keyword(s):  
Feature Fusion ◽  
Recognition Accuracy ◽  
Dynamic Change ◽  
Expression Recognition ◽  
Change Rate ◽  
Evaluation Approach ◽  
High Death Rate ◽  
High Death ◽  
Micro Expression

Depression leads to a high suicide rate and a high death rate. But the disease can be cured if recognized in time. At present, there are only a few low-precision methods for recognizing mental health or mental disorder. Therefore, this paper attempts to recognize elderly depression by extracting facial micro-expressions. Firstly, a micro-expression recognition model was constructed for elderly depression recognition. Then, a jump connection structure and a feature fusion module were introduced to VGG-16 model, realizing the extraction and classification of micro-expression features. After that, a quantitative evaluation approach was proposed for micro-expressions based on the features of action units, which improves the recognition accuracy of elderly depression expressions. Finally, the advanced features related to the dynamic change rate of depression micro-expressions were constructed, and subjected to empirical modal decomposition (EMD) and Hilbert analysis. The effectiveness of our algorithm was proved through experiments.

scholarly journals Cross-Database Micro-Expression Recognition Exploiting Intradomain Structure

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yanliang Zhang ◽  
Ying Liu ◽  
Hao Wang
Keyword(s):  
Model Selection ◽  
Computational Efficiency ◽  
Recognition Accuracy ◽  
Psychological State ◽  
Expression Recognition ◽  
Labor Costs ◽  
Practical Applications ◽  
The Past ◽  
Micro Expression ◽  
High Computational Efficiency

Micro-expressions are unconscious, faint, short-lived expressions that appear on the faces. It can make people's understanding of psychological state and emotion more accurate. Therefore, micro-expression recognition is particularly important in psychotherapy and clinical diagnosis, which has been widely studied by researchers for the past decades. In practical applications, the micro-expression recognition samples used in training and testing are from different databases, which causes the feature distribution between the training and testing samples to be different to a large extent, resulting in a drastic decrease in the performance of the traditional micro-expression recognition methods. However, most of the existing cross-database micro-expression recognition methods require a large number of model selection or hyperparameter tuning to select better results from them, which consumes a large amount of time and labor costs. In this paper, we overcome this problem by exploiting the intradomain structure. Nonparametric transfer features are learned through intradomain alignment, while at the same time, a classifier is learned through intradomain programming. In order to evaluate the performance, a large number of cross-database experiments were conducted in CASMEII and SMIC databases. The comparison of results shows that this method can achieve a promising recognition accuracy and with high computational efficiency.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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