Information Content Detection in Face Parts Using Deep Learning

Abstract

The present paper introduces use of deep neural network for classification of three different categories of emotions - angry, happy and neutral. The database consisted of 48x48 pixel grayscale images of faces from the Face expression recognition dataset from Kaggle. Separate parts of faces such as eyes, nose, or mouths were occluded by a manually inserted 48x15 pixel black rectangle to see what part of the face carries the most significant information about the expressed emotions. By applying pretrained Inception network provided as a part of Keras/Tensorflow environment, we found that, to our surprise, faces with eyes covered were more easily identified. Results were replicated using augmented data.