# A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Proposed Hybrid Model Architecture

#### 3.1. Image Pre-processing

#### 3.1.1. Image Resizing

#### 3.1.2. Noise Removal

#### 3.1.3. Median Filter

#### 3.1.4. Histogram Equalization

#### 3.1.5. Wiener Filter

#### 3.2. Face and Facial Components Detection

#### 3.3. Feature Extraction

_{1}= x cos θ + y sin θ, R

_{2}= −x sin θ + y cos θ, σ

_{1}= c

_{1}/f, σ

_{2}= c

_{2}/f, ${f}_{x}=f$ cos θ, ${f}_{y}=f\mathrm{sin}\mathsf{\theta},$c

_{1}and c

_{2}are two constants.

^{2}=$\iint}hh\ast dxdy=1$.

#### Linear Binary Pattern

- (i)
- Select a window of pixel with size mxn with the pixel intensity values ranging from 0 to 255.
- (ii)
- Split the window into individual cells.
- (iii)
- For every pixel in a given cell, the pixel is compared with its eight neighbors in a clockwise circular direction.
- (iv)
- If the pixel value at the center is greater than its neighbor pixel value, then the value is set to zero; otherwise, it will be set to one. An eight-digit binary number will be generated from each window.
- (v)
- Histogram with dimensions of mxn over the cell is computed, repeated for every combination, and normalized the resultant histogram.
- (vi)
- Concatenate all the normalized histograms can generate a feature vector.

#### 3.4. Deep Network as Features Descriptor

#### 3.5. Support Vector Machine (SVM) Classifier

_{i}$\in $ R

^{n}, i = 1,…k and the respective set of ‘k’ labels y

_{i}$\in \left\{1,-1\right\}$ then optimization will be carried out by

## 4. Experimental Results

#### 4.1. Implementation Details

#### 4.2. Dataset description

#### 4.2.1. FER 2013 Dataset

#### 4.2.2. CK+ Dataset

#### 4.2.3. KDEF Dataset

#### 4.2.4. KMU-FED Dataset

#### 4.3. Performance Evaluation

#### 4.3.1. Experiments on FER 2013 Dataset

#### 4.3.2. Experiments on CK+ Dataset

#### 4.3.3. Experiments on KDEF Dataset

#### 4.3.4. Experiments on KMU-FED Dataset

#### 4.4. Emotion Recognition Results

#### 4.5. Limitations

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AAA | American Automobile Association |

ADAS | Advanced Driver Assistance Systems |

CNN | Convolutional Neural Network |

DL | Deep Learning |

E.C.G | Electrocardiogram |

E.D.A | Electrical Dermal Activity |

E.E.G | Electroencephalogram |

FER | Face Expression Recognition |

LBP | Linear Binary Pattern |

ML | Machine Learning |

P.P.G | Photoplethysmography |

ReLU | Rectified Linear Unit |

ROI | Region of Interest |

S.G.D | Stochastic Gradient Descent |

SVM | Support Vector Machine |

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**Figure 4.**Confusion matrices of the proposed method with accuracy (%) using (

**a**) F.E.R. 2013 dataset, (

**b**) CK+ dataset, (

**c**) KDEF dataset, and (

**d**) KMU-FED dataset.

Databases | Parameters | Settings |
---|---|---|

Image Size | 256 × 256 | |

Optimizer | Stochastic Gradient Descent (S.G.D.) | |

CK+, | Loss Function | Cross Entropy |

FER 2013, | Activation Function | ReLU |

KDEF, | Batch Size | 128 |

KMUFED | Learning rate | 0.001 |

Epochs | 100 | |

Momentum | 0.9 | |

Validation Frequency | 30 |

Databases | Parameters | Settings |
---|---|---|

Objective Function | Hinge loss | |

CK+, | Solver | SGD |

FER 2013, | Kernel | Linear |

KDEF, KMUFED | Type | One-vs-one |

Comparison Methods | Accuracy (%) |
---|---|

CNN-MNF [37] | 70.3 |

CNN-BOVW-SVM [36] | 75.4 |

KCNN-SVM [39] | 80.3 |

VCNN [30] | 65.7 |

EXNET [31] | 73.5 |

Deep-Emotion [32] | 70.0 |

IRCNN-SVM [42] | 68.1 |

GLFCNN+SVM (Our Proposed Approach) | 84.4 |

Comparison Methods | Accuracy (%) |
---|---|

Inception-Resnet and LSTM [35] | 93.2 |

DCMA-CNN [27] | 93.4 |

WRF [22] | 92.6 |

LMRF [24] | 93.4 |

VGG11+SVM [40] | 92.2 |

DNN+RELM [43] | 86.5 |

LBP+ORB+SVM [25] | 93.2 |

MDNETWORK [33] | 96.2 |

GLFCNN+SVM (Our Proposed Approach) | 95.1 |

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## Share and Cite

**MDPI and ACS Style**

Sukhavasi, S.B.; Sukhavasi, S.B.; Elleithy, K.; El-Sayed, A.; Elleithy, A. A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach. *Int. J. Environ. Res. Public Health* **2022**, *19*, 3085.
https://doi.org/10.3390/ijerph19053085

**AMA Style**

Sukhavasi SB, Sukhavasi SB, Elleithy K, El-Sayed A, Elleithy A. A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach. *International Journal of Environmental Research and Public Health*. 2022; 19(5):3085.
https://doi.org/10.3390/ijerph19053085

**Chicago/Turabian Style**

Sukhavasi, Suparshya Babu, Susrutha Babu Sukhavasi, Khaled Elleithy, Ahmed El-Sayed, and Abdelrahman Elleithy. 2022. "A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach" *International Journal of Environmental Research and Public Health* 19, no. 5: 3085.
https://doi.org/10.3390/ijerph19053085