A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Robust Real-Time Face Detection
International Journal of Computer Vision
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection with boosted Gaussian features
Pattern Recognition
Adjusted pixel features for robust facial component classification
Image and Vision Computing
Illumination invariant face alignment using multi-band active appearance model
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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Analyzing human faces is a traditional topic in computer vision research. For this task, model based approaches have been proven adequate to extract high-level information in many applications. However, they require a robust estimation of model parameters to work reliably. To tackle this challenge, we train displacement experts that serve as an update function on initial model parameter configurations. Unfortunately, building displacement experts that work robustly even in unconstrained environments is a non-trivial task. Therefore, we rely on a priori information about the structure of human faces by integrating an image representation that reflects the location of several facial components, so called "multi-band images". By combining multi-band images and learned displacement experts, we propose a novel face model fitting approach. An evaluation on the "Labeled Faces In The Wild" database demonstrates, that this approach provides robust fitting results even in unconstrained environments.