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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 6730249, 11 pages
Research Article

Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures

1Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea
2Computer Graphics and Multimedia Group, RWTH Aachen University, Lehrstuhl für Informatik 8, 52056 Aachen, Germany

Received 17 May 2015; Accepted 11 October 2015

Academic Editor: Fivos Panetsos

Copyright © 2016 Hyun-Chul Choi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.