Facial feature location with Delaunay triangulation/Voronoi diagram calculation
VIP '01 Proceedings of the Pan-Sydney area workshop on Visual information processing - Volume 11
An accurate active shape model for facial feature extraction
Pattern Recognition Letters
Automatic detection of face and facial features
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Classification in data mining for face images using neuro:genetic approaches
International Journal of Artificial Intelligence and Soft Computing
Reliable and fast eye detection
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Real-time detection of face and iris
WSEAS Transactions on Signal Processing
Dimensionality reduction via genetic value clustering
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Feature-adaptive motion energy analysis for facial expression recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Artificial Life and Robotics
Towards race-related face identification: research on skin color transfer
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Emotional human-machine interaction: cues from facial expressions
HI'11 Proceedings of the 2011 international conference on Human interface and the management of information - Volume Part I
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Facial feature extraction using a probabilistic approach
Image Communication
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An automatic facial feature extraction algorithm is presented. The algorithm is composed of two main stages: the face region estimation stage and the feature extraction stage. In the face region estimation stage, a second-chance region growing method is adopted to estimate the face region of a target image. In the feature extraction stage, genetic search algorithms are applied to extract the facial feature points within the face region. It is shown by simulation results that the proposed algorithm can automatically and exactly extract facial features with limited computational complexity