Independent component analysis: algorithms and applications
Neural Networks
Robust Real-Time Face Detection
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Pose Angle Determination by Face, Eyes and Nose Localization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Feature-Based Detection of Facial Landmarks from Neutral and Expressive Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Facial Feature Points Detection
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Facial feature detection using distance vector fields
Pattern Recognition
3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial feature extraction using PCA and wavelet multi-resolution images
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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In this paper, a robust fully automatic method for nose field detection under different imaging conditions is presented. It depends on the local appearance and shape of nose region characterized by edge information. Independent Components Analysis (ICA) is used to learn the appearance of nose. We show experimentally that using edge information for characterizing appearance and shape outperforms using intensity information. The influence of preprocessing step on the performance of the method is also examined. A subregion-based framework depending on statistical analysis of intensity information in the nose region is proposed to improve the efficiency of ICA. Experimental results show that the proposed method can accurately detect nose with an average detection rate of 95.5% on 6778 images from six different databases without prior detection for other facial features, outperforming existing methods.