A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Linear Dimensionality Reduction
Advances in Neural Information Processing Systems 5, [NIPS Conference]
On the Combination of Different Template Matching Strategies for Fast Face Detection
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Locating Facial Region of a Head-and-Shoulders Color Image
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Advances in Component Based Face Detection
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-stage combination of geometric and colorimetric detectors for eyes localization
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Face detection and tracking in a video by propagating detection probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adapted active appearance models
Journal on Image and Video Processing
Generic facial encoding for shape alignment with active models
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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 a new method dedicated to the localization of faces in color images. It combines a connexionist model (auto-associative network), an ellipse model based on Generalized Hough Transform, a skin color model and an eyes detector that results in two features. A linear combination of the 3 first models is performed to eliminate most of non face regions. A connexionist combination of the four detectors response is performed on the remaining candidates. Given an input image, we compute a kind of probability map on it with a sliding window. The face position is then determined as the location of the absolute maximum over this map. Improvement of baseline detectors localization rates is clearly shown and results are very encouraging.