Dependency:

Required libraries and packages can be download from requirements.txt

pip install -r requirements.txt

We have used Smiles dataset which contains 13,165 grayscale images in the dataset, with each image having a size of 64×64 pixels from which 9,475 of these examples are not smiling while only 3,690 belong to the smiling class

We used train_model.py to train the network. This file takes two command line arguments --dataset is the path to the SMILES directory residing on disk and --model is the path to where the serialized LeNet weights will be saved after training

to run train_model.py, insert following command

python train_model.py --dataset datasets/SMILEsmileD --model output/lenet.hdf5

We used detect_smile.py to detect the face in videostream or web-cam to predict smiling or not-smiling at real time

To run detect_smile.py using your webcam, execute the following command

python detect_smile.py --cascade haarcascade_frontalface_default.xml --model output/lenet.hdf5

If you instead want to use a video file you would update your command to use the --video switch

python detect_smile.py --cascade haarcascade_frontalface_default.xml --model output/lenet.hdf5 --video path/to/your/video.mov