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.
@article{arxiv.1701.01879,
title = {Greedy Search for Descriptive Spatial Face Features},
author = {Caner Gacav and Burak Benligiray and Cihan Topal},
journal= {arXiv preprint arXiv:1701.01879},
year = {2017}
}
Comments
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017