Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Multi-View Faces in Real-Time
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Robust Real-Time Face Detection
International Journal of Computer Vision
A Multiscale and Multidirectional Image Denoising Algorithm Based on Contourlet Transform
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
An Improvement of AdaBoost for Face-Detection with Motion and Color Information
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Face Recognition Based on Curvefaces
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Multi-view Face Detection Based on the Enhanced AdaBoost Using Walsh Features
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Classification-based face detection using gabor filter features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Novel face detection method based on gabor features
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
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This paper primarily investigates a novel face detection method based on contourlet features. In this method, a face-pyramid is developed through contourlet transform, which includes both low and high frequency information to represent face features on multiresolutions and multidirections. The most discriminative features are then selected from the face-pyramid and are trained to construct the classifier by using the cascade boosting algorithm (Adaboost). Speed and capability are important issues for current face detection systems. This method extensively reduces feature demensions and the negative sample numbers step by step, so that the speed is increased radically. Mean-face template matching is adopted finally in the system to ensure a detection of one face in a scanned image. Extensive experiments are conducted and the results show that the proposed method is efficient in detecting frontal faces from cluttered images.