|Autors: Sechkova, T. G., Tonchev K., Manolova, A. H.|
Title: Action Unit recognition in still images using graph-based feature selection
Keywords: Action Unit recognition;Supervised Gradient Descent
Abstract: Facial expressions are universal and independent of race, culture, ethnicity, nationality, gender, age, religion, or any other demographic variable. In this paper, we propose a Facial Action Unit recognition algorithm using graph-based feature selection in unsupervised and supervised setting. The proposed algorithm is based on a state of the art algorithm for facial key points detection - Supervised Gradient Descent method, the classification is carried out using the well know Support Vector Machines classifier. Built this way, the algorithm works on still images where the human expressions are expected to be in their apex phase. Using leave one person out evaluation methodology we achieve average accuracy of 90.1% for unsupervised and 92.7% for supervised feature selection on 12 Action Units.
Full text of the publication
1. He, Jun, Xiaocui Yu, Bo Sun, and Yongkang Xiao. "Facial action units recognition by de-expression residue learning." In Optoelectronic Imaging and Multimedia Technology VI, vol. 11187, p. 1118719. International Society for Optics and Photonics - 2019 - в издания, индексирани в Scopus или Web of Science
Вид: пленарен доклад в международен форум, публикация в реферирано издание