Deep learning algorithms for emotion recognition on low power single board computers

Abstract

In the world of Human-Computer Interaction, a computer should have the ability to communicate with humans. One of the communication skill that a computer requires is to recognize the emotional state of the human. With the state-of-the-art computing systems along with Graphical Processing Units, a Deep Neural Network can be realized by training on any publicly available dataset and learn the whole emotion estimation into one single network. In a real-time application, the inference of such a network may not need high computational power as training a network does. Several Single Board Computers (SBC) such as Raspberry Pi is now available with sufficient computational power wherein during inference; small Deep Neural Networks models could perform well enough with acceptable accuracy and processing delay. The paper deals in exploring SBC capabilities for DNN inference, where we prepare a target platform on which real-time camera sensor data is processed to detect face regions and succeed further with recognizing emotions. Several DNN architectures are evaluated on SBC considering processing delay, possible frame rates and classification accuracy on SBC. Finally, a Neural Compute Stick (NCS) such as Intel’s Movidius is used to look at the performance of SBC for Emotion classification.