Digital image processing
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection using multimodal density models
Computer Vision and Image Understanding
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Classification-based face detection using gabor filter features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
A multi-expert approach for wavelet-based face detection
Pattern Recognition Letters
Visual affect recognition
Appearance-based face detection with artificial neural networks
Intelligent Decision Technologies
Palmprint recognition using polynomial neural network
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
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Both detection accuracy and speed are of major concerns in developing a robust face detection system for real-world applications. To this end, we propose a robust face detection approach by combining multiple experts in both cascade and parallel manner. We design three detection experts which employ different feature representation schemes of local images: 2D Haar wavelet, gradient direction, and Gabor filter. The three features are classified using the same classification model, namely, a polynomial neural network (PNN) on reduced feature subspace. The detection experts are used in multiple stages with simple ones in proceeding stages and complex ones in succeeding stages for improving detection speed. Meanwhile, the output of each expert is combined with the outputs of its preceding experts to improve detection accuracy. The effectiveness of the multi-expert approach has been demonstrated in experiments on a large number of images. The obtained detection results are superior to the best individual expert and state-of-the-art approaches while the detection speed is fast.