A Generative Shape Regularization Model for Robust Face Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Non-rigid face tracking with enforced convexity and local appearance consistency constraint
Image and Vision Computing
Coarse-to-fine statistical shape model by Bayesian inference
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Robust shape-based head tracking
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Personalized 3D-aided 2D facial landmark localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Kalman filter-based facial emotional expression recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Face alignment using boosting and evolutionary search
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network more robust to local image noise. We propose a hierarchical Condensation algorithm to draw the shape samples efficiently. Specifically, a proposal density incorporating the local face shape is designed to generate more samples close to the image features for accurate alignment, based on a local Markov network search. A constrained regularization algorithm is also developed to weigh favorably those points that are already accurately aligned. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.