In this project we explore human recognition system to identify 7 types of emotions by using FER2013 dataset. We aim to classify the emotion on a person's face into one of seven categories, using CNN model features to a Long Short-Term Memory(LSTM) networks. This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.
The repository is currently compatible with tensorflow-2.0 and makes use of the Keras API using the tensorflow.keras library.
- The original FER2013 dataset in Kaggle is available as a single csv file.
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First, the haar cascade method is used to detect faces in each frame of the webcam feed.
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The region of image containing the face is resized to 48x48 and is passed as input to the CNN.
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Then we extract features from CNN and pass into LSTM Model.
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The network outputs a list of softmax scores for the seven classes of emotions.
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The emotion with maximum score is displayed on the screen.
