Coarse-to-fine statistical shape model by Bayesian inference
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Automatic closed eye correction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Robust face alignment based on hierarchical classifier network
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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Active Shape Model (ASM) has been shown to be a powerful tool to aid the interpretation of images, especially in face alignment. ASM local appearance model parameter estimation is based on the assumption that residuals between model fit and data have a Gaussian distribution. Moreover, to generate an allowable face shape, ASM truncates coefficients of shape principal components into the bounds determined by eigenvalues. In this paper, an algorithm of modeling local appearances, called AdaBoosted ASM, and a shape parameter optimization method are proposed. In the algorithm of modeling the local appearances, we describe our novel modeling method by using AdaBoosted histogram classifiers, in which the assumption of the Gaussian distribution is not necessary. In the shape parameter optimization, we describe that there is an inadequacy on controlling shape parameters in ASM, and our novel method on how to solve it. Experimental results demonstrate that the AdaBoosted histogram classifiers improve robustness of landmark displacement greatly, and the shape parameter optimization solves the inadequacy problem of ASM on shape constraint effectively.