An introduction to wavelets
An introduction to computational learning theory
An introduction to computational learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Optimal sampling of Gabor features for face recognition
Pattern Recognition Letters
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Face Detection Using Boosting in Hierarchical Feature Spaces
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simplified Gabor wavelets for human face recognition
Pattern Recognition
Fast Asymmetric Learning for Cascade Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Electronics in Agriculture
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Combining texture and stereo disparity cues for real-time face detection
Image Communication
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Face-detection methods based on cascade architecture have demonstrated a fast and robust performance. In most of these methods, each node of the cascade employs the simple Haar-like features from the central eye-nose-mouth region using the boosting method. However, it can be empirically observed that, in the deeper nodes of the boosting process, the non-face examples collected by bootstrapping are in fact very similar to the face examples, and the error rate of those feature-based weak classifiers is very close to 50%. Consequently, the performance of the face detector is hardly further improved. In this paper, we propose a novel and simple solution to this problem by imitating the characteristics of the human visual system. The main idea of our solution is to boost the cascade based on a hierarchical strategy, which employs the information from the central and surrounding parts of the face regions step by step. We argue that the context information about a face can be advantageously used in the deeper nodes of the boosting process when the features derived from the central region of the face do not provide any further benefit. Furthermore, we also propose a simplified Gabor feature to extend the feature set for the training of deeper nodes. Experiments show that our proposed method can improve not only the detection performance, but also the detection speed, by about 10% when compared to the original AdaBoost face-detection method for our implementation.