A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Digital Image Processing in Java
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Probabilistic Hierarchical Face Model for Feature Localization
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
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IMVIP '08 Proceedings of the 2008 International Machine Vision and Image Processing Conference
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Probabilistic Corner Detection for Facial Feature Extraction
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Component-based face recognition with 3D morphable models
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Facial component detection for efficient facial characteristic point extraction
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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This paper presents a new approach for fitting a 3D morphable model to images of faces, using self-adapting feature layers (SAFL). The algorithm integrates feature detection into an iterative analysis-by-synthesis framework, combining the robustness of feature search with the flexibility of model fitting. Templates for facial features are created and updated while the fitting algorithm converges, so the templates adapt to the pose, illumination, shape and texture of the individual face. Unlike most existing feature-based methods, the algorithm does not search for the image locations with maximum response, which may be prone to errors, but forms a tradeoff between feature likeness, global feature configuration and image reconstruction error. The benefit of the proposed method is an increased robustness of model fitting with respect to errors in the initial feature point positions. Such residual errors are a problem when feature detection and model fitting are combined to form a fully automated face reconstruction or recognition system. We analyze the robustness in a face recognition scenario on images from two databases: FRGC and FERET.