Digital image processing
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Planar Object Classes
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Locating Facial Region of a Head-and-Shoulders Color Image
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Detection Based on Color and Local Symmetry Information
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Learning and example selection for object and pattern detection
Learning and example selection for object and pattern detection
A statistical approach to 3d object detection applied to faces and cars
A statistical approach to 3d object detection applied to faces and cars
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Classifier combination for face localization in color images
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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Computer-based face perception is becoming increasingly important for many applications like biometric face recognition, video coding or multi-model human-machine interaction. Fast and robust detection and segmentation of a face in an unconstrained visual scene is a basic requirement for all kinds of face perception. This paper deals with the integration of three simple visual cues for the task of face detection in grey level images. It is achieved by a combination of edge orientation matching, hough transform and an appearance based detection method. The proposed system is computationally efficient and has proved to be robust under a wide range of acquisition conditions like varying lighting, pixel noise and other image distortions. The detection capabilities of the presented algorithm are evaluated on a large database of 13122 images including the frontal-face set of the m2vts database. We achieve a detection rate of over 91% on this database while having only few false detects at the same time.