This project particularly aims on the classification of a person's face into one of seven categories, using deep convolution neural networks.
- Facial emotion recognition is the process of detecting human emotions from facial expressions.
- The human brain recognizes emotions automatically, and software has now been developed that can recognize emotions as well
- The idea here is to build a deep learning model which is able to predict emotions of people at real time. We can classify our data into 7 different emotions(Angry,Disgust,Fear,Happy,Sad,Surprise,Neutral)
- Pandas library is used for reading the dataset stored in csv file.
- Numpy library is used for converting data into image format.
- Opencv library is used for reading image data and passing that data into our custom deep learning model.
- Tensorflow and Keras library were used to build and train the model.
- Matplotlib library is used to visualize the data and training curves.
- The original FER2013 dataset in Kaggle is available as a single csv file. I had converted into a dataset of images for training/testing. The dataset can be downloaded from https://www.kaggle.com/deadskull7/fer2013
- Clone the repository using git clone
git clone https://github.com/rnag5076/Emotion-Recognition.git
cd Emotion-Recognition- Run the real_time.py file which will load my customized model and will predict the emotions in real time through webcam.
python real_time.py- If you want to generate the model you can use emotion_analysis.py and run it to see the results as well.
python emotion_analysis.py