Feature extraction from faces using deformable templates
International Journal of Computer Vision
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
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Journal of Cognitive Neuroscience
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Parts-based segmentation with overlapping part models using Markov chain Monte Carlo
Image and Vision Computing
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Face localization via hierarchical CONDENSATION with fisher boosting feature selection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Personalized 3D-aided 2D facial landmark localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
An integrated model for accurate shape alignment
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Face alignment under various poses and expressions
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Robust face alignment based on hierarchical classifier network
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
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Many approaches have been proposed to locate faces in an image. There are, however, two problems in previous facial shape models using feature points. First, the dimension of the solution space is too big since a large number of key points are needed to model a face. Second, the local features associated with the key points are assumed to be independent. Therefore, previous approaches require good initialization (which is often done manually), and may generate inaccurate localization. To automatically locate faces, we propose a novel hierarchical shape model (HSM) or multi-resolution shape models corresponding to a Gaussian pyramid of the face image. The coarsest shape model can be quickly located in the lowest resolution image. The located coarse model is then used to guide the search for a finer face model in the higher resolution image. Moreover, we devise a Global and Local (GL) distribution to learn the likelihood of the joint distribution of facial features. A novel hierarchical data-driven Markov chain Monte Carlo (HDDMCMC) approach is proposed to achieve the global optimum of face localization. Experimental results demonstrate that our algorithm produces accurate localization results quickly, bypassing the need for good initialization.