Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures
Joint Authors
Choi, Hyun-Chul
Sibbing, Dominik
Kobbelt, Leif
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Choi, Hyun-Chul& Sibbing, Dominik& Kobbelt, Leif. 2015. Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099737
Modern Language Association (MLA)
Choi, Hyun-Chul…[et al.]. Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099737
American Medical Association (AMA)
Choi, Hyun-Chul& Sibbing, Dominik& Kobbelt, Leif. Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures. Computational Intelligence and Neuroscience. 2015. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099737
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099737