tharunc/facial-expression-recognition-classifier-model

An Exciting Deep Learning Model that takes Real-time Video input, predicts the Facial Expressions of users and also does Graphical Visualization of the Expressions !

😎 Facial Expression Recognition Classifier Model

A Facial Expression Recognition Classifier Model that takes Real-time Video input and predicts the Emotion of users present in front of Webcam. It also gives Graphical Visualization of Expressions when we feed in an Image via Web Cam or manually !

➿ Tech Stack used

• Deep Learning Techniques : Convolutional Nueral Networks (CNN)
• Python
• Flask

You can watch the Project Demo Video 🎥 here

⛳ Predictions done by Model

Predictions On Live Image

💥 Steps to run the Project in your local device !!

• Fork this repository.
• Clone the repository to your System using git clone
• Example : git clone https://github.com<your-github-username>/Facial-Expression-Recognition-Classifier-Model
• Open any Python IDE and run the fer_main.py file in the FER_on_Real_Time_Video_Input Folder to make Facial Expression Recognition of Live Image taken using your Web Cam !
• In case, If you want to try with Images you already had in your system follow the below steps
• Just upload them in static folder of the Folder FER_on_Manually_Uploaded_Image
• Now open any Python IDE and run the fer_main_Manual_U.py file.

💻 Coding Structure:

• Import the required Packages and Libraries.
• Data analysis and Creating Training and Validation Batches.
• Create a CNN using 4 Convolutional Layers including Batch Normalization, Activation, Max Pooling, Dropout Layers followed by Flatten Layer, 2 Fully Connected dense Layers and finally Dense Layer with SoftMax Activation Function.
• Compile the model using Adam Optimizer and categorical cross entropy loss function.
• Training the model for 15 epochs and then Evaluating the model as well as saving the model Weights in .h5 Values
• Saving the model as JSON string.
• Creating a Class in a separate file to reload the model and its weights to make predictions and return the probabilities of each emotion.
• Creating one more class in a Separate file which takes in the Real-time Video input and returns frames of Images with a Circle detecting the face and putting text of its emotion on it.
• A python script is also created which upon running yields the Graphical Visualization of Emotions present in the Image provided.
• Finally creating a file which inherits form all the Classes defined by us and deploys our application using Flask.

😇 We can further improve the Validation Accuracy of the model by tuning the hyperparameters like:

• Learning Rate
• Epochs
• Batch Size
• Number of Layers in CNN
• Number of filters
• Size of filters
• Value in Dropout Layers
• Optimizers

Future Scope of the Project ❗

1. Making the Frontend Attractive 🔥
2. Suggesting Music based on Facial Emotion predicted 🎵
3. Deploying the Model 🌏

Steps to Contribute to this Project ! 👇

Go through the link If you are new to Open Source Contribution here on making your First Contribution !!

• Fork this repository
• Clone the repository to your System using git clone https://github.com<your-github-username>/Facial-Expression-Recognition-Classifier-Model
• Create a branch :-
  • Change to the repository directory on your computer cd Facial-Expression-Recognition-Classifier-Model
  • Now create a branch using the git checkout command: git checkout -b your-new-branch-name
• Make changes as per your requirement to solve the Issues mentioned in the Future scope of the Project and commit those changes.
• If you go to the project directory and execute the command git status, you'll see there are changes. Add those changes to the branch you just created using the git add
• Now commit those changes using the git commit command: git commit -m "Added the feature of Suggesting Music"
• Push your changes to GitHub using the command git push origin <add-your-branch-name>
• If you go to your repository on GitHub, you'll see a Compare & pull request button. Click on that button.
• Now describe the changes you made and submit the pull request.
• Wait for the Maintainers to review :)

Any type of Contributions like Pull Requests, Issues are always welcomed ! 🎉