Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images
Abstract
:1. Introduction
- We have developed a 3D CNN model architecture for micro-expression recognition which is able to extract spatial and temporal features simultaneously;
- A novel pre-processing technique is employed by selecting the Apex frame sequence from the entire video, where the timestamp of the most pronounced emotion is centered within this sequence;
- Stratified K-fold was applied for model evaluation because it is suitable for small datasets with imbalanced class distribution as in our case;
- Comprehensive experimental validation was performed by comparing the proposed model with two reimplemented state-of-the-art methods in intra-dataset as well as cross-dataset evaluations in a total of eight different scenarios. To the best of our knowledge, such an extensive evaluation in this or comparable manner has not been conducted for micro-expression recognition so far.
2. Related Works
2.1. Handcrafted Methods
2.2. Deep Learning-Based Methods
3. Datasets
3.1. CASME II
3.2. SMIC
3.3. SAMM
4. Pre-Processing
4.1. Face Detection and Alignment
4.2. Facial Landmark Detection
4.3. Apex Frame Spotting
4.4. Selection of Apex Frame Sequence
5. Network Architectures
5.1. Model-A (Proposed 3D CNN Model)
5.2. Split-Model
5.3. Model-B
5.4. Model-C
6. Model Training Parameters
6.1. Model-A
6.2. Model-B
6.3. Model-C
7. Experimental Analysis
8. Results And Discussions
8.1. Train–Test Split
8.2. Stratified K-Fold
8.2.1. Scenario-1
8.2.2. Scenario-2
8.2.3. Scenario-3
8.2.4. Scenario-4
8.2.5. Scenario-5
8.2.6. Scenario-6
8.2.7. Scenario-7
8.2.8. Scenario-8
9. Applications and Use Cases
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
3D CNN | 3D Convolutional Neural Networks |
3DHOG | 3D Histogram-Oriented Gradients |
ANN | Artificial Neural Networks |
AU | Action Unit |
CASME | Chinese Academy of Sciences Micro-Expression |
CASME II | Chinese Academy of Sciences Micro-Expression II |
CNN | Convolutional Neural Networks |
EVM | Eulerian Video Magnification |
FACS | Facial Action Coding System |
FPS | Frames per second |
HOG | Histogram of Gradients |
LBP | Local Binary Pattern |
LBP-TOP | Local Binary Pattern histograms from Three Orthogonal Planes |
LSTM | Long Short-Term Memory |
MMOD | max-margin object-detection algorithm |
ReLU | Rectified Linear Unit |
ROI | Region of Interest |
SAM | Self-Assessment Manikins |
SAMM | Spontaneous Actions and Micro-Movements |
SGD | Stochastic Gradient Descent |
SMIC | Spontaneous Micro-Expression Corpus |
SVM | Support Vector Machine |
TIM | Temporal interpolation model |
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Emotions | CASME II | SMIC | SAMM |
---|---|---|---|
Happy | 32 | 51 | 26 |
Disgust | 63 | 70 | 15 |
Surprise | 28 | 43 | 9 |
Total | 123 | 164 | 50 |
Layer Type | Filter Size | Output Shape |
---|---|---|
Conv3D-1 | 3 × 3 × 3 | 36 × 128 × 128 × 16 |
BatchNorm-1 | - | 36 × 128 × 128 × 16 |
3D-MaxPooling-1 | 3 × 3 × 3 | 12 × 42 × 42 × 16 |
Dropout-1 | - | 12 × 42 × 42 × 16 |
Conv3D-2 | 3 × 3 × 3 | 12 × 42 × 42 × 16 |
BatchNorm-2 | - | 12 × 42 × 42 × 16 |
3D-MaxPooling-2 | 3 × 3 × 3 | 4 × 14 × 14 × 16 |
Dropout-2 | - | 4 × 14 × 14 × 16 |
Flatten | - | 12,544 |
Dense-1 | - | 128 |
Dropout-3 | - | 128 |
Dense-2 | - | 3 |
Type of Frame Sequence | ||
---|---|---|
Dataset | Initial Frame Sequence | Apex Frame Sequence |
CASME II | 46.9% | 56.5% |
SMIC | 34.3% | 43.7% |
Scenario | Train | Test | Model-A | Model-B | Model-C |
---|---|---|---|---|---|
01 | SMIC | SMIC | 43.7% | 33.5% | 37.3% |
02 | CASME II | CASME II | 56.5% | 45.4% | 48.1% |
03 | Combined | Combined | 88.2% | 85.4% | 80.4% |
04 | SMIC | SAMM | 44.3% | 31.1% | 42.0% |
05 | CASME II | SAMM | 24.8% | 24.3% | 23.1% |
06 | SMIC | CASME II | 44.7% | 43.7% | 39.1% |
07 | CASME II | SMIC | 37.7% | 35.4% | 36.5% |
08 | Combined | SAMM | 27.1% | 23.1% | 36.9% |
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Talluri, K.K.; Fiedler, M.-A.; Al-Hamadi, A. Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images. Appl. Sci. 2022, 12, 11078. https://doi.org/10.3390/app122111078
Talluri KK, Fiedler M-A, Al-Hamadi A. Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images. Applied Sciences. 2022; 12(21):11078. https://doi.org/10.3390/app122111078
Chicago/Turabian StyleTalluri, Kranthi Kumar, Marc-André Fiedler, and Ayoub Al-Hamadi. 2022. "Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images" Applied Sciences 12, no. 21: 11078. https://doi.org/10.3390/app122111078
APA StyleTalluri, K. K., Fiedler, M. -A., & Al-Hamadi, A. (2022). Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images. Applied Sciences, 12(21), 11078. https://doi.org/10.3390/app122111078