Subpixel subtle motion estimation of micro-expressions multiclass classification

Muna, Niyalatul; Rosiani, Ulla Delfana; Yuniamo, Eko Mulyanto; Pumomo, Mauridhi Hery

doi:10.1109/siprocess.2017.8124558

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Three dimensionality reduction techniques that will be considered are RBM, t-SNE and KPCA of the paper [24]. Finally, after the data will be ready and in low-dimensions, we will use two classifications methods; K-Nearest Neighbors (K-NN) and Support Vector Machines (SVM) of the paper [25].…”

Section: A Data Analysesmentioning

confidence: 99%

Dimensionality Reduction: A Comparative Review using RBM, KPCA, and t-SNE for Micro-Expressions Recognition

Bakiasi,

Muça,

Kapçiu

2024

IJACSA

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Facial expressions are the main ways how humans display emotions. Under certain circumstances, humans can do facial expression, but emotions can also appear in the special form of micro-expressions. A micro-expression is a very brief facial expression faced on people's faces under some circumstances. Micro-expressions are shown in the situations when a person tries to lie or hide something. Studying microexpressions sounds very attractive but considering the number of pixels that an image contains becomes difficult. Feature extraction techniques are the most popular ones for reducing data dimensionality. Those techniques create a new lowdimensional dataset, which tries to represent as much information as original dataset. Many and many methods are used for dimensionality reduction. Restricted Boltzmann Machine (RBM), Kernel Principal Component Analyses (KPCA) and t-distributed stochastic neighbor embedding (t-SNE) are currently widely used by researchers. Choosing the right dimensionality reduction technique is time consuming. This study proposes one framework for micro-expression recognition. The two key processes of this framework are the facial feature extraction (Dlib) and dimensionality reduction using RBM, KPCA and t-SNE. We will select the technique that generates new dataset which represents as much the original dataset as possible. The framework will be trained with images from the CASMEII database, which is a database built specially for research purposes. The framework will be tested with new images unseen before. Software used for conducting the experiments is Python.

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Section: A Data Analysesmentioning

confidence: 99%

Dimensionality Reduction: A Comparative Review using RBM, KPCA, and t-SNE for Micro-Expressions Recognition

Bakiasi,

Muça,

Kapçiu

2024

IJACSA

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“…Four microexpression classes were used for all experiments (disgust, happiness, surprise, and sadness) via a multi-classification approach. The motion feature from the POC-ABS feature extraction method is recognized using Supports Vector Machine (SVM)[ [35] with Radial Base Function (RBF) [6] and K-Nearest Neighbor (KNN) with Euclidean distance metric [36]. In this study, ten-fold crossvalidation was used to train and test all samples of each class.…”

Section: Micro-expression Classificationmentioning

confidence: 99%

“…Observation of subtle and fast motion is needed in the automatic detection system of microexpression [6]. Various video analysis applications such as detection of moving objects [7], human motion detection [8], hand gesture analysis [9], cinematography [10], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Another faster and accurate approach is the block-based motion estimation or Block Matching Algorithm (BMA) [20]. Combining BMA method that uses Sum Absolute Difference (SAD) technique and simplified Optical Flow approach, Taylor Series Approximation can better classify emotions on micro-expression with the best accuracy was 85.07% [6]. There are some popular methods of matching criteria or distortion functions for BMA: SAD, Mean of Absolute Difference (MAD), Mean Squared Error (MSE) and Phase Only Correlation (POC).…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Manifold feature integration for micro-expression recognition

Takalkar

Chaczko

2020

Multimedia Systems

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Subpixel subtle motion estimation of micro-expressions multiclass classification

Cited by 7 publications

References 14 publications

Dimensionality Reduction: A Comparative Review using RBM, KPCA, and t-SNE for Micro-Expressions Recognition

Dimensionality Reduction: A Comparative Review using RBM, KPCA, and t-SNE for Micro-Expressions Recognition

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

Manifold feature integration for micro-expression recognition

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