The Usage of Grayscale or Color Images for Facial Expression Recognition with

Recognition with Deep Neural Networks
Dmitry A. Yudin1, Alexandr V. Dolzhenko2

and Ekaterina O. Kapustina2

1 Moscow Institute of Physics and Technology (National Research University),
2 Belgorod State Technological University named after V.G. Shukhov, Belgorod,
*yudin.da@mipt.ru

Dataset: 0 – Neutral, 1 – Happiness, 2– Sadness, 3

– Surprise, 4 – Fear, 5 – Disgust, 6 – Anger, 7 – Contempt

To solve the task it is necessary to develop various variants of deep neural network architectures and to test them on the available data set with 1-channel (grayscale) and 3-channel (color) image representation.
We must determine which image representation is best used for the task of facial expression recognition. Also, we need to select the best architecture that will provide best performance and the highest quality measures of image classification: accuracy, precision and recall

Dropout – setting rectangular areas within images to zero. We

have generated a dropout mask at 2 to 25 percent of image's size. In that mask, 0 to 2 percent of all pixels were dropped (random per image).
Affine transformation – image rotation on random degrees from -15 to 15.
Flipping of image along vertical axis with 0.9 probability.
Addition Gaussian noise to image with standard deviation of the normal distribution from 0 to 15.
Cropping away (cut off) random value of pixels on each side of the image from 0 to 10% of the image height/width.

inspired from ResNet

is based on DenseNet169 model

with changed input tensor to 120x120x3 for color images and

120x120x1 for grayscale images

architectures

english:
https://mipt.ru/english/research/labs/cds