GREEDY SEARCH FOR DESCRIPTIVE SPATIAL FACE FEATURES
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Louisiana, United States Of America, 5 - 09 March 2017, pp.1497-1501, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/icassp.2017.7952406
- City: Louisiana
- Country: United States Of America
- Page Numbers: pp.1497-1501
- Keywords: facial expression recognition, spatial features, sequential forward selection
- Open Archive Collection: AVESIS Open Access Collection
- Anadolu University Affiliated: Yes
Abstract
Facial expression recognition methods use a combination of geometric and appearance-based features. Spatial features are derived from displacements of facial landmarks, and carry geometric information. These features are either selected based on prior knowledge, or dimension-reduced from a large pool. In this study, we produce a large number of potential spatial features using two combinations of facial landmarks. Among these, we search for a descriptive subset of features using sequential forward selection. The chosen feature subset is used to classify facial expressions in the extended Cohn-Kanade dataset (CK+), and delivered 88.7% recognition accuracy without using any appearance-based features.