Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Model for the Human Skin Color
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
Combination of Image Registration Algorithms for Patient Alignement in Proton Beam Therapy
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
A Real Time Fingers Detection by Symmetry Transform Using a Two Cameras System
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Visual Servoing for Patient Alignment in ProtonTherapy
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A Real-Time Road Sign Detection Using Bilateral Chinese Transform
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
A new voting algorithm for tracking human grasping gestures
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Classifier combination for face localization in color images
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Multiple neural networks for facial feature localization in orientation-free face images
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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We present in this paper a method for the localization of the eyes in a facial image. This method works on color images, applying the so called Chinese Transformation (CT) on edge pixels to detect local symmetry. The CT is combined with a skin color model based on a modified Gaussian Mixture Model (GMM). The CT and the modified GMM give us a small rectangular area containing one eye with a very high probability. This rectangle is then processed to find the precise position of the eye, using four sources of information: a darkness measure, a circle finder, a “not skin” finder and a position information. Experimental results on a large database are presented on nearly 1000 faces from the ECU database.