Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People
2. General Description of the VR Platform
2.1. High-Level Architecture
3. UAV Simulator
- Quadrotor’s Dynamic Model: It mathematically represents how the lift forces of the quadrotor change when the rotational speed of its four propellers are modified, thus achieving the three possible motions; pitch, roll and yaw. It was obtained following the Euler–Lagrange formulation according to  and can be consulted in [13,15]. Please note that the output of this component is the state of the UAV, its position and orientation, information that is sent to the VR Visualiser in order to reproduce the flight of the UAV by updating the position and orientation of a 3D virtual quadrotor.
- Control Algorithm: It is used to calculate what inputs should be applied to the quadrotor model in order to follow a specific trajectory reference, thereby ensuring that the UAV is correctly positioned and oriented during the monitoring flight. For this component, we designed a generalised proportional integral (GPI) controller based on the flatness theory that demonstrated good results in both stabilisation and tracking tasks, even in the presence of atmospheric disturbances and noise measurements, improving the performance of a traditional PID controller [16,17,18,19,20,21,22,23]. The theoretical details of the GPI control scheme are described in .
- Trajectory Planner: On the basis of the person’s position and orientation, which are received from the avatar in the VR Visualiser, a state-machine-based planner generates the references for the position and yaw angle of the UAV for each of the manoeuvres that make up the monitoring process. In the current implementation of the planner, it is possible to configure the tracking trajectory to define the height at which the UAV flies, as well as the trajectory (circular or elliptical) it describes around the person, to suit the user’s preferences. Details of this planner can be found in .
4. VR Visualiser
5. ER System
5.1. Design of Emotions in Avatars
5.2. Transitions Between Emotions
5.3. Face Detection Algorithm
5.3.1. Cascade Classifiers
5.3.2. Implemented Solution
5.4. Design of the Convolutional Neural Network (CNN)
5.4.1. Convolutional Neural Network
5.4.2. Implemented Solution
- Convolutional Layers: These are responsible for deriving features from the spatial dependencies between pixels in the image, generating multiple filters that produce a feature map. Several of these layers are usually included (sometimes back to back) to capture as much information as possible. The first layers detect simple shapes such as lines and curves while the later layers are more specialised and can recognise complex shapes. However, it is not advisable to add too many layers because, at some point, they do not significantly improve the model and only increase its complexity and computational time.
- Subsampling Layers (such as MaxPooling or AveragePooling): These are included after the convolutional layers to reduce the number of parameters generated and subsequently reduce the overfitting of the model.
- Flatten Layer: It converts the output of the convolutions into a vector used as the input of the final stage of the network, the fully connected layers.
- Fully Connected Layers (FCL): These are typically used to calculate probabilities and have an input layer, one or more hidden layers and an output layer.
- Dropout Layers: These are placed between the fully connected layers to remove a percentage of their neurons and reduce overfitting.
- “Conv (1)”: The first convolutional layer has 64 kernels and uses ReLU as an activation function. Its input size is 48 × 48 × 1 as the input images are 48 pixels wide by 48 pixels high with only one colour channel (black and white).
- “MaxP”: A MaxPooling layer with two displacement units.
- “Conv (2)”: A second convolutional layer with 64 kernels and ReLU as activation function.
- “Conv (3)”: A third convolutional layer just like the previous one.
- “AvgP (1)”: A first AveragePooling layer with two displacement units.
- “Conv (4)”: A fourth convolutional layer with 128 kernels and ReLU as activation function.
- “Conv (5)”: A fifth convolutional layer identical to the previous one.
- “AvgP (2)”: A second AveragePooling layer with two displacement units
- “Flatten”: A layer which takes the output from the convolutional layers and converts it to an input vector for the fully connected layers where the classification is finished.
- “Input (FCL)”: The first fully connected layer with 1024 neurons which takes the inputs from the feature analysis and applies the weights to predict the correct label.
- “Drop (1)”: A first Dropout layer to get rid of 20% of the neurons and reduce overfitting.
- “Hidden (FCL)”: A hidden fully connected layer with the same number of neurons as the input.
- “Drop (2)”: A second layer of Dropout with the same characteristics as the previous one.
- “Output (FCL) ”: The output layer where a Softmax function is run to convert the output into a probability distribution of size 7 (equal to the number of classes to be classified, i.e., the six basic emotions plus neutral).
5.5. Definition of Neural Network Training
6. Experimental Results
6.1. UAV Simulator Data
6.2. Integration of the ER System into the VR Platform
6.3. Performance Test of the Emotion Detector
7. Conclusions and Future Work
Conflicts of Interest
|CNN||Convolutional Neural Network|
|FCL||Fully Connected Layer|
|FDR||False Discovery Rate|
|FNR||False Negative Rate|
|GPI||Generalised Proportional Integral|
|MQTT||Message Queue Telemetry Transport|
|PPV||Positive Predictive Value (or Precision)|
|TNR||True Negative Rate (or Specificity)|
|TPR||True Positive Rate (or Recall)|
|UAV||Unmanned Aerial Vehicle|
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|Conv (1)||Convolutional||44 × 44 × 64||1664|
|MaxP||MaxPooling||20 × 20 × 64||0|
|Conv (2)||Convolutional||18 × 18 × 64||36,928|
|Conv (3)||Convolutional||16 × 16 × 64||36,928|
|AvgP (1)||AveragePooling||7 × 7 × 64||0|
|Conv (4)||Convolutional||5 × 5 × 128||73,856|
|Conv (5)||Convolutional||3 × 3 × 128||147,584|
|AvgP (1)||AveragePooling||1 × 1 × 128||0|
|Input (FCL)||Fully Connected||1024||132,096|
|Hidden (FCL)||Fully Connected||1024||1,049,600|
|Output (FCL)||Fully Connected||7||7175|
|Class||Images||Accuracy, ACC||Recall, TPR||Precision, PPV||Specificity, TNR||F1 Score|
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Martínez, A.; Belmonte, L.M.; García, A.S.; Fernández-Caballero, A.; Morales, R. Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People. Electronics 2021, 10, 868. https://doi.org/10.3390/electronics10070868
Martínez A, Belmonte LM, García AS, Fernández-Caballero A, Morales R. Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People. Electronics. 2021; 10(7):868. https://doi.org/10.3390/electronics10070868Chicago/Turabian Style
Martínez, Anselmo, Lidia M. Belmonte, Arturo S. García, Antonio Fernández-Caballero, and Rafael Morales. 2021. "Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People" Electronics 10, no. 7: 868. https://doi.org/10.3390/electronics10070868