title: Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures.
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.
authors.affiliation: Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea.
authors.forename: Hyun-Chul
authors.initials: HC
authors.lastname: Choi
authors.affiliation: Computer Graphics and Multimedia Group, RWTH Aachen University, Lehrstuhl für Informatik 8, 52056 Aachen, Germany.
authors.forename: Dominik
authors.initials: D
authors.lastname: Sibbing
authors.affiliation: Computer Graphics and Multimedia Group, RWTH Aachen University, Lehrstuhl für Informatik 8, 52056 Aachen, Germany.
authors.forename: Leif
authors.initials: L
authors.lastname: Kobbelt
mesh_terms: D000465:Algorithms; D005145:Face; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D009949:Orientation / Q000502:physiology; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual / Q000502:physiology*; D025341:Principal Component Analysis; D018709:Statistics, Nonparametric
references.citation: Image Vis Comput. 2009 Nov 1;27(12):1804-1813
references.pmid: 20046797