Face localization via hierarchical CONDENSATION with Fisher boosting feature selection

TLDR: Experiments show that, Fisher boosting algorithm can generate strong classifier with less number of weaker classifiers comparing to conventional Adaboosting algorithm, that the face localization with Fisher boosting feature subspace outperforms that with PCA feature subspaces in localization accuracy and convergence rate, and that the design of hierarchical CONDENSATION framework alleviates the local minima problem which is frequently encountered by previous ASM optimization algorithms.

Abstract: We formulate face localization as a maximum a posteriori probability (MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribution for intrinsic face configuration is defined by active shape model (ASM). The likelihood model for local facial features is parameterized as mixture of Gaussians in feature space. A hierarchical CONDENSATION framework is then proposed to estimate the face configuration parameter. In order to improve the discriminative power of likelihood distribution in feature space, a new feature subspace, Fisher boosting feature space, is proposed and compared against PCA subspace and biased PCA subspace. Experiments show that, Fisher boosting algorithm can generate strong classifier with less number of weaker classifiers comparing to conventional Adaboosting algorithm as illustrated in a toy problem, that the face localization with Fisher boosting feature subspace outperforms that with PCA feature subspaces in localization accuracy and convergence rate, and that the design of hierarchical CONDENSATION framework alleviates the local minima problem which is frequently encountered by previous ASM optimization algorithms.