/Facial_Emotion_Recognition

Project aimed to compare the performances of MLP, SVM and CNN algorithms in detecting the facial emotions. It also uses a pre-trained model and evaluates its performance in facial emotion recognition.

Primary LanguageJupyter Notebook

Facial_Emotion_Recognition

Project aimed to compare the performances of MLP, SVM and CNN algorithms in detecting the facial emotions. It also uses a pre-trained model and evaluates its performance in facial emotion recognition. The highest accuracy was achieved by the pretrained CNN model in detecting emotions.

Dataset - RAF Dataset

File Contents -

  1. SVM_MLP_Train.ipynb - Google Colaboratory file to train MLP and SVM models. Both models are tried with different combinations of feature descriptors like SIFT, and HOG.
  2. CNN_Train.ipynb - Google Colaboratory file to construct and train a Convolutional Neural Network.
  3. Test_Emotion_Recognition_RAFDataset_SVM_MLP.ipynb - Google Colaboratory file to test the MLP, SVM and CNN models on the test RAF dataset and note down the accuracies of the models.
  4. Test_Emotion_Recognition_Video_CNN.ipynb - Google Colaboratory file to test the MLP, SVM and CNN models on a YouTube video.