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- Title
Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures.
- Authors
Choi, Hyun-Chul; Sibbing, Dominik; Kobbelt, Leif
- Abstract
We present a nonparametric facial feature localization method using relative directional information between regularly sampled image segments and facial feature points. Instead of using any iterative parameter optimization technique or search algorithm, our method finds the location of facial feature points by using a weighted concentration of the directional vectors originating from the image segments pointing to the expected facial feature positions. Each directional vector is calculated by linear combination of eigendirectional vectors which are obtained by a principal component analysis of training facial segments in feature space of histogram of oriented gradient (HOG). Our method finds facial feature points very fast and accurately, since it utilizes statistical reasoning from all the training data without need to extract local patterns at the estimated positions of facial features, any iterative parameter optimization algorithm, and any search algorithm. In addition, we can reduce the storage size for the trained model by controlling the energy preserving level of HOG pattern space.
- Subjects
SEARCH algorithms; IMAGE segmentation; MULTIPLE correspondence analysis (Statistics); MATHEMATICAL optimization; FACE; PHYSIOLOGY
- Publication
Computational Intelligence & Neuroscience, 2015, Vol 2015, p1
- ISSN
1687-5265
- Publication type
Article
- DOI
10.1155/2016/6730249