Face Recognition by Elastic Bunch Graph Matching
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
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Real Time Visual Cues Extraction for Monitoring Driver Vigilance
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Unified Learning Framework for Real Time Face Detection and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Viewer-Centered Object Recognition in Monkeys
Viewer-Centered Object Recognition in Monkeys
Editorial: special issue: eye detection and tracking
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
An improved likelihood model for eye tracking
Computer Vision and Image Understanding
EURASIP Journal on Applied Signal Processing
Robust feature detection for facial expression recognition
Journal on Image and Video Processing
Automatic eye winks interpretation system for human-machine interface
Journal on Image and Video Processing
Faces of pain: automated measurement of spontaneousallfacial expressions of genuine and posed pain
Proceedings of the 9th international conference on Multimodal interfaces
Robust hand tracking in low-resolution video sequences
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Emotionally aware automated portrait painting
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
Social signal processing: Survey of an emerging domain
Image and Vision Computing
Automatic coding of facial expressions displayed during posed and genuine pain
Image and Vision Computing
Emotionally Intelligent Agents for Human Resource Management
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Multi-modal features for real-time detection of human-robot interaction categories
Proceedings of the 2009 international conference on Multimodal interfaces
Classification of facial expressions using K-nearest neighbor classifier
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Hi-index | 0.00 |
We formulate a probabilistic model of image generation and derive optimal inference algorithms for finding objects and object features within this framework. The approach models images as a collage of patches of arbitrary size, some of which contain the object of interest and some of which are background. The approach requires development of likelihood-ratio models for object versus background generated patches. These models are learned using boosting methods. One advantage of the generative approach proposed here is that it makes explicit the conditions under which it is optimal. We applied the approach to the problem of finding faces and eyes on arbitrary images. Optimal inference under the proposed model works in real time and is robust to changes in lighting, illumination, and differences in facial structure, including facial expressions and eyeglasses. Furthermore, the system can simultaneously track the eyes and blinks of multiple individuals. Finally we reflect on how the development of perceptive systems like this may help advance our understanding of the human brain.