Artificial intelligence is the mainstream solution to various problems, and thanks to developments in hardware, it is possible to achieve performance like never before. Continuous data collection provides us with opportunities for the creation of various datasets, which are the basis for various challenges. One of those challenges is recognizing emotions from humans’ facial expressions. Multiple deep learning models exist in the wild to solve such a task. They always yield high accuracy on their respective validation and test set. However, the performance of such a model tends to decrease when used on real-world images. This work gives insight into how such a deep learning model can predict facial expressions from biases present in training data.
What makes a smile? A Deep Neural Network Point of View
Published in:
IEEE
ISBN:
978-1-7281-8053-3