Neural Network-Based Face Detection
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
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Detection Based on Color and Local Symmetry Information
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Fast Anchor Shot Detection Algorithm on Compressed Video
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A robust method for detecting arbitrarily tilted human faces in color images
Pattern Recognition Letters
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Face detection is a key problem in human-computer interaction. In this paper, we present an algorithm for rotation invariant face detection in color images of cluttered scenes. It is a hierarchical approach, which combines a skin color model, a neural network, and an upright face detector. Firstly, the skin color model is used to process the color image to segment the face-like regions from the background. Secondly, the neural network computing and an operation for locating irises are performed to acquire rotation angle of each input window in the face-like regions. Finally, we provide an upright face detector to determine whether or not the rotated window is a face. Those techniques are integrated into a face detection system. The experiments show that the algorithm is robust to different face sizes and various lighting conditions.