Face Expression Recognizer is an Application which takes a video stream as input and predict the emotions of persons in video. This application is built using Keras and Flask is used for deployment. The Model achieves an accuracy of 77% with 100 epochs of training. The batch size used is 64.
The Dataset used for Training the model is from fer2013 which consist of 35886 images of seven universal emotions( Anger, Disgust, Fear, Happy, Neutral, Sad, Surprise). The Dataset is divided in 80% training images and 20% testing images. The images are 40x40 Grayscale images.
Some images in dataset are following:
Model: "sequential"
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Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 48, 48, 64) 640
_________________________________________________________________
batch_normalization (BatchNo (None, 48, 48, 64) 256
_________________________________________________________________
activation (Activation) (None, 48, 48, 64) 0
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 24, 24, 64) 0
_________________________________________________________________
dropout (Dropout) (None, 24, 24, 64) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 24, 24, 128) 204928
_________________________________________________________________
batch_normalization_1 (Batch (None, 24, 24, 128) 512
_________________________________________________________________
activation_1 (Activation) (None, 24, 24, 128) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 12, 12, 128) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 12, 12, 128) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 12, 12, 512) 590336
_________________________________________________________________
batch_normalization_2 (Batch (None, 12, 12, 512) 2048
_________________________________________________________________
activation_2 (Activation) (None, 12, 12, 512) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 512) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 6, 6, 512) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 6, 6, 512) 2359808
_________________________________________________________________
batch_normalization_3 (Batch (None, 6, 6, 512) 2048
_________________________________________________________________
activation_3 (Activation) (None, 6, 6, 512) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 3, 3, 512) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 3, 3, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 4608) 0
_________________________________________________________________
dense (Dense) (None, 256) 1179904
_________________________________________________________________
batch_normalization_4 (Batch (None, 256) 1024
_________________________________________________________________
activation_4 (Activation) (None, 256) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 256) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 131584
_________________________________________________________________
batch_normalization_5 (Batch (None, 512) 2048
_________________________________________________________________
activation_5 (Activation) (None, 512) 0
_________________________________________________________________
dropout_5 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 7) 3591
=================================================================
Total params: 4,478,727
Trainable params: 4,474,759
Non-trainable params: 3,968
Once You have cloned the Face Expression Recognizer then run the follwing command:
cd \path\to\Project\
pip install pipenv
pipenv install
pipenv run python3 main.py
Before running these commmand make sure that the path of video is correct in camera.py file. If you want to use webcam set path to 0.
After running these command open in browser 0.0.0.0:5000 to see the face reccognizer work.
If you want to train the model yourself then first unzip the test and train folder and then run facial_expression_training.py
unzip train.zip
unzip test.zip
python3 facial_expression_training.py