Multi-Attribute Robust Facial Feature Localization
Source: Bogazici University
In this paper, the authors focus on the reliable detection of facial fiducial points, such as eye, eyebrow and mouth corners. The proposed algorithm aims to improve automatic landmarking performance in challenging realistic face scenarios subject to pose variations, high-valence facial expressions and occlusions. They explore the potential of several feature modalities, namely, Gabor Wavelets, Independent Component Analysis (ICA), Non-negative Matrix Factorization (NMF), and Discrete Cosine Transform (DCT), both singly and jointly. They show that the selection of the highest scoring face patch as the corresponding landmark is not always the best, but that there is considerable room for improvement with the cooperation among several high scoring candidates and also using a graph-based post-processing method.