Face recognition via AAM and multi-features fusion on riemannian manifolds

Authors:
Hongwen Huo;Jufu Feng
Affiliations:
Key Laboratory of Machine Perception (Peking University), MOE, Department of Machine Intelligence School of Electronics Engineering and Computer Science, Peking University, Beijing, P.R China;Key Laboratory of Machine Perception (Peking University), MOE, Department of Machine Intelligence School of Electronics Engineering and Computer Science, Peking University, Beijing, P.R China
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Year:
2009

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Abstract

We develop a novel face recognition algorithm which is robust to random position perturbations of key points and does not require face alignment, e.g resizing, rotating, cropping, etc In our proposed method, a well trained Active Appearance Model (AAM) is first divided into several regions by special landmarks, and each region is given a label by a template This model is then fed to new coming facial images to segment the images into irregular regions In these regions, multi-features fusion matrices are calculated and embedded to related Riemannian manifolds to train classifiers which are combined to construct a final classifier Our experiment results show its accuracy, efficiency, and robustness on FERET and A-R human face database.