Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers
Pattern Recognition Letters
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Rapidly Trainable and Global Illumination Invariant Object Detection System
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
An improved Adaboost.R algorithm and its application in mining safety monitoring
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Automatic eye detection using intensity filtering and K-means clustering
Pattern Recognition Letters
Fast feature selection and training for AdaBoost-based concept detection with large scale datasets
Proceedings of the international conference on Multimedia
LACBoost and FisherBoost: optimally building cascade classifiers
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Journal of Real-Time Image Processing
Asymmetric totally-corrective boosting for real-time object detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Face detection with effective feature extraction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Improving detector of Viola and Jones through SVM
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
TAKES: a fast method to select features in the kernel space
Proceedings of the 20th ACM international conference on Information and knowledge management
Probabilistic cascade random fields for man-made structure detection
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
Efficient and accurate face detection using heterogeneous feature descriptors and feature selection
Computer Vision and Image Understanding
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Adaptive Haar-like classifier for eye status detection under non-ideal lighting conditions
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
ACM Computing Surveys (CSUR)
Novel adaptive eye detection and tracking for challenging lighting conditions
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Generalized mean for feature extraction in one-class classification problems
Pattern Recognition
Robotics and Autonomous Systems
A neural-AdaBoost based facial expression recognition system
Expert Systems with Applications: An International Journal
An SVM-AdaBoost facial expression recognition system
Applied Intelligence
| Hi-index | 0.15 |
A cascade face detector uses a sequence of node classifiers to distinguish faces from non-faces. This paper presents a new approach to design node classifiers in the cascade detector. Previous methods used machine learning algorithms that simultaneously select features and form ensemble classifiers. We argue that if these two parts are decoupled, we have the freedom to design a classifier that explicitly addresses the difficulties caused by the asymmetric learning goal. There are three contributions in this paper. The first is a categorization of asymmetries in the learning goal, and why they make face detection hard. The second is the Forward Feature Selection (FFS) algorithm and a fast pre- omputing strategy for AdaBoost. FFS and the fast AdaBoost can reduce the training time by approximately 100 and 50 times, in comparison to a naive implementation of the AdaBoost feature selection method. The last contribution is Linear Asymmetric Classifier (LAC), a classifier that explicitly handles the asymmetric learning goal as a well-defined constrained optimization problem. We demonstrated experimentally that LAC results in improved ensemble classifier performance.