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3-D Moment Forms: Their Construction and Application to Object Identification and Positioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric invariants and object recognition
International Journal of Computer Vision
Nonorthogonal image expansion related to optimal template matching in complex images
CVGIP: Graphical Models and Image Processing
Human face recognition and the face image set's topology
CVGIP: Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
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Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Probabilistic Analysis of Regularization
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)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Efficient detection under varying illumination conditions and image plane rotations
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Shape-Based Recognition of Wiry Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Pattern Matching Using Projection Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Dependent Representations for Illumination Insensitive Image Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
On the Design of Cascades of Boosted Ensembles for Face Detection
International Journal of Computer Vision
Automatic cascade training with perturbation bias
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
AdaBoost face detection on the gpu using Haar-like features
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Over-complete wavelet approximation of a support vector machine for efficient classification
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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This paper offers a novel detection method, which works well even in the case of a complicated image collection驴for instance, a frontal face under a large class of linear transformations. It is also successfully applied to detect 3D objects under different views. Call the collection of images, which should be detected, a multitemplate. The detection problem is solved by sequentially applying very simple filters (or detectors), which are designed to yield small results on the multitemplate (hence, 驴antifaces驴), and large results on 驴random驴 natural images. This is achieved by making use of a simple probabilistic assumption on the distribution of natural images, which is borne out well in practice. Only images which passed the threshold test imposed by the first detector are examined by the second detector, etc. The detectors are designed to act independently so that their false alarms are uncorrelated; this results in a false alarm rate which decreases exponentially in the number of detectors. This, in turn, leads to a very fast detection algorithm. Typically, $(1+\delta)N$ operations are required to classify an N-pixel image, where $\delta