Identifying Facial Emotions by detecting faces in real time using web cam.
- To identify different faces in the frame
- Identify their expressions
- Apply ML and DL Models to predict their emotions
Note: Kindly do not push any changes to Main or Master Branch. Create a New Branch and push all the changes to that branch.
Don't forget to create an issue before making a PR.
👉 Repo Link : https://github.com/Ayush12062000/Real-Time-Emotion-Detection.git
- About Project
- Languages or Frameworks Used
- Setup
Facial Emotion Detection is the process of detecting human emotion from facial expressions. The human brain detects emotions automatically, and software has now been developed that can recognize emotions as well.
The Dataset used is already present in Kaggle you can download the dataset from here - https://www.kaggle.com/deadskull7/fer2013 , It contains 48*48 px grayscale images. There are 7 classes/categories in this dataset (0=angry, 1=disgust, 2=fear, 3=happy, 4=sad, 5=surprise, 6=neutral). Then, I Build the model with three convolution layers. And used Opencv to detect emotions in Real Time.
- Python: language
- NumPy: library for numerical calculations
- Pandas: library for data manipulation and analysis
- Tensorflow: library for large numerical computations without keeping deep learning in mind
- Keras: neural network library
- First Clone the repository.
- Create and activate the virtual environment for the project.
$ python -m venv Project_emotion $ Project_emotion\Scripts\activate.bat - Install the required packages using requirements.txt inside the environemnt using pip.
$ pip install -r requirements.txt - run the Emotion_Detection_1.ipynb in google colab (make sure GPU/TPU is enabled).
- then, run Real_Time_Detection.py on your local machine.
Refer this for detailed explaination: 👁 https://valueml.com/emotion-detection-using-cnn-in-python-using-keras/