Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A statistical approach to 3d object detection applied to faces and cars
A statistical approach to 3d object detection applied to faces and cars
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
International Journal of Computer Vision
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Hybrid Generative-Discriminative Visual Categorization
International Journal of Computer Vision
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Putting Objects in Perspective
International Journal of Computer Vision
Boosted Bayesian network classifiers
Machine Learning
PittPatt Face Detection and Tracking for the CLEAR 2007 Evaluation
Multimodal Technologies for Perception of Humans
A conditional independence algorithm for learning undirected graphical models
Journal of Computer and System Sciences
An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Structure learning for activity recognition in robot assisted intelligent environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
PittPatt face detection and tracking for the CLEAR 2006 evaluation
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
A framework of CBIR system based on relevance feedback
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Facial contour labeling via congealing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Robust facial landmark detection for intelligent vehicle system
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Object detection via fusion of global classifier and part-based classifier
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Efficient object-class recognition by boosting contextual information
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Robotics and Autonomous Systems
Automatic facial expression recognition in real-time from dynamic sequences of 3D face scans
The Visual Computer: International Journal of Computer Graphics
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Many classes of images have the characteristics of sparse structuring of statistical dependency and the presence of conditional independencies among various groups of variables. Such characteristics make it possible to construct a powerful classifier by only representing the stronger direct dependencies among the variables. In particular, a Bayesian network compactly represents such structuring. However, learning the structure of a Bayesian network is known to be NP complete. The high dimensionality of images makes structure learning especially challenging. This paper describes an algorithm that searches for the structure of a Bayesian network based classifier in this large space of possible structures. The algorithm seeks to optimize two cost functions: a localized error in the log-likelihood ratio function to restrict the structure and a global classification error to choose the final structure of the Network. The final network structure is restricted such that the search can take advantage of pre-computed estimates and evaluations. We use this method to automatically train detectors of frontal faces, eyes, and the iris of the human eye. In particular, the frontal face detector achieves state-of-the-art performance on the MIT-CMU test set for face detection.