Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Efficient Visual Recognition Using the Hausdorff Distance
Efficient Visual Recognition Using the Hausdorff Distance
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
ENCARA2: Real-time detection of multiple faces at different resolutions in video streams
Journal of Visual Communication and Image Representation
The state of play in machine/environment interactions
Artificial Intelligence Review
Fast frontal-view face detection using a multi-path decision tree
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Adapting hausdorff metrics to face detection systems: a scale-normalized Hausdorff distance approach
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
A robust two stage approach for eye detection
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Face detection in low-resolution color images
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
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In our previous work we presented a model-based approach to perform robust, high-speed face localization based on the Hausdorff distance. A crucial step during the design of the system is the choice of an appropriate edge model that fits for a wide range of different human faces. In this paper we present an optimization approach that creates and successively improves such a model by means of genetic algorithms. To speed up the process and to prevent early saturation we use a special bootstrapping method on the sample set. Several initialization functions are tested and compared.