Comparison of Angle and Size Features with Deep Learning for Emotion Recognition

The robust recognition of a person's emotion from images is an important task in human-machine interaction. This task can be considered a classification problem, for which a plethora of methods exists. In this paper, the emotion recognition performance of two fundamentally different approaches is compared: classification based on hand-crafted features against deep learning. This comparison is conducted by means of well-established datasets and highlights the benefits and drawbacks of each approach.