Keras best Image Data Generator parameters for data augmentation

Question

I'm working on facial expression recognition using Keras, the dataset I'm using does not have a big amount of data available, So I'm going to use Keras's image preprocessing for data augmentation.

I want to know the best parameters of ImageDataGenerator to generate normal faces wich I can use to train my neural network with.

Here's the code I'm using for Data augmentation :

def data_augmentation(subdir):

    datagen = ImageDataGenerator(
        featurewise_center=False,
        samplewise_center=False,
        featurewise_std_normalization=False,
        samplewise_std_normalization=False,
        zca_whitening=False,
        rotation_range=30,
        width_shift_range=0.2,
        height_shift_range=0.2,
        horizontal_flip=True,
        vertical_flip=False)

    print ("\nData augmentation...")
    print ("\nProcess...")

    for file in glob.glob(subdir+"*/*.jpg"):
        img = load_img(file)
        print ("\nProcessing..." + str(file))
        x = img_to_array(img)
        x = x.reshape((1,) + x.shape)

        i = 0
        for batch in datagen.flow(x, batch_size=1, save_to_dir='data_aug', save_prefix='Fig', save_format='jpg'):
            i += 1
            if i > 20:
                break

Here's all ImageDataGenerator's parameters

keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,
                samplewise_center=False,
                featurewise_std_normalization=False,
                samplewise_std_normalization=False,
                zca_whitening=False,
                zca_epsilon=1e-6,
                rotation_range=0.,
                width_shift_range=0.,
                height_shift_range=0.,
                shear_range=0.,
                zoom_range=0.,
                channel_shift_range=0.,
                fill_mode='nearest',
                cval=0.,
                horizontal_flip=False,
                vertical_flip=False,
                rescale=None,
                preprocessing_function=None,
                data_format=K.image_data_format())

And here's an example of images generated using my code :

As you can see, the images are distorted and not good enough to train my network.

I want to know what's the best parameters of ImageDataGenerator for human faces or is there any better methods for data augmentation ?

Answer 1

If you are dealing with faces only, check this paper: http://www.openu.ac.il/home/hassner/projects/augmented_faces/ It can generate 30 images from a single face image. Also you can use virtual makeup implementations in python. They are easy to use and successful.