Robust Facial Representation for Recognition

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Abstract

One of the main challenges in face recognition lies in robust representation of facial images in unconstrained real-world environment, where face appearances of a same person often vary significantly. This thesis investigates both holistic and local feature based representations, and develops several novel representation models in an effort to mitigate within-person variations and enhance discriminative power.The work first focuses on feature extraction of high-dimensional holistic representation based on intensities. Several linear and nonlinear dimensionality reduction methods are systematically compared. One of key findings is that linear PCA has comparable performances to the most recent nonlinear methods for extracting low-dimensional facial features. Extensive experiments are conducted and results are presented to support the findings, together with a quantitative measure of nonlinearity showing theoretical insights. Following these findings, a robust framework combining an automatic outlier detector and a nearest subspace classifier, is presented. The detector computes the corrupted regions of face images by measuring their reconstructive capabilities, while the classifier models face data by multiple linear subspaces.

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