Facial Expression Recognition on a Quantum Computer
This paper addresses the problem of facial expression recognition for the computer vision community, demonstrating a potential quantum computing solution.
This paper explores facial expression recognition using a quantum machine learning approach, where facial expressions are represented as graphs and classified by a quantum circuit. The quantum classifier's accuracy was evaluated on a quantum simulator and compared to a classical classifier.
We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a given dataset, our approach substantially exploits quantum interference. By representing face expressions via graphs, we define a classifier as a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states. We discuss the accuracy of the quantum classifier evaluated on the quantum simulator available on the IBM Quantum Experience cloud platform, and compare it with the accuracy of one of the best classical classifier.