Active shape models—their training and application
Computer Vision and Image Understanding
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating Facial Features in Image Sequences using Neural Networks
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Hierarchical Wavelet Networks for Facial Feature Localization
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Journal of Cognitive Neuroscience
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Age invariant face verification with relative craniofacial growth model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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In this paper, we proposed a new facial landmark-detection system using as edge energy function. The facial landmark-detection system is divided into a learning stage and a detection stage. The learning stage creates an interest-region model, to set up a search region of each landmark, as preinformation necessary for a detection stage and creates a detector for each landmark to detect a landmark in a search region. The detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. Because a landmark to detect from a system has the characteristics of an edge as both edge of an eye, both edge of a mouth and both edges of eyebrows, we have detected a landmark by applying an edge energy function to the Bayesian discrimination method. We have implemented aforementioned technique by abstracting 800 impassive images from the FERET database and have measured data in which the normalized average error distance is less than 0.1 occupying 98% of the total data.