Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Robust Object Detection via Soft Cascade
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Description of interest regions with local binary patterns
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization of a training set for more robust face detection
Pattern Recognition
Combining appearance and motion for face and gender recognition from videos
Pattern Recognition
Haar-like features with optimally weighted rectangles for rapid object detection
Pattern Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Cost-Sensitive Boosting: Fitting an Additive Asymmetric Logistic Regression Model
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Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning Discriminative Features Based on Distribution
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Image Processing
Face detection based on multi-block LBP representation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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The discriminative power of a feature has an impact on the convergence rate in training and running speed in evaluating an object detector. In this paper, a novel distribution-based discriminative feature is proposed to distinguish objects of rigid object categories from background. It fully makes use of the advantage of local binary pattern (LBP) that specializes in encoding local structures and statistic information of distribution from training data, which is utilized in getting optimal separating hyperplane. The proposed feature maintains the merit of simplicity in calculation and powerful discriminative ability to distinguish objects from background patches. Three LBP-based features are derived to adaptive projection ones, which are more discriminative than original versions. The asymmetric Gentle Adaboost organized in nested cascade structure constructs the final detector. The proposed features are evaluated on two different object categories: frontal human faces and side-view cars. Experimental results demonstrate that the proposed features are more discriminative than traditional Haarlike features and multi-block LBP (MBLBP) features. Furthermore they are also robust in monotonous variations of illumination.