Lighting and pose robust face sketch synthesis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Expression flow for 3D-aware face component transfer
ACM SIGGRAPH 2011 papers
Personalized 3D-aided 2D facial landmark localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Face analysis using curve edge maps
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Real-Time Facial Feature Tracking on a Mobile Device
International Journal of Computer Vision
Joint face alignment: rescue bad alignments with good ones by regularized re-fitting
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Bayesian face revisited: a joint formulation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Interactive facial feature localization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Robust 3D human face reconstruction by consumer binocular-stereo cameras
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
Face Alignment by Explicit Shape Regression
International Journal of Computer Vision
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In this paper, we propose a component-based discriminative approach for face alignment without requiring initialization. Unlike many approaches which locally optimize in a small range, our approach searches the face shape in a large range at the component level by a discriminative search algorithm. Specifically, a set of direction classifiers guide the search of the configurations of facial components among multiple detected modes of facial components. The direction classifiers are learned using a large number of aligned local patches and misaligned local patches from the training data. The discriminative search is extremely effective and able to find very good alignment results only in a few (2~3) search iterations. As the new approach gives excellent alignment results on the commonly used datasets (e.g., AR [18], FERET [21]) created under-controlled conditions, we evaluate our approach on a more challenging dataset containing over 1,700 well-labeled facial images with a large range of variations in pose, lighting, expression, and background. The experimental results show the superiority of our approach on both accuracy and efficiency.