Pose-invariant face recognition with parametric linear subspaces

We present a framework for pose-invariant face recognition using parametric linear subspace models as stored representations of known individuals. Each model can be fit to an input, resulting in faces of known people whose head pose is aligned to the input face. The model's continuous nature enables the pose alignment to be very accurate, improving recognition performance, while its generalization to unknown poses enables the models to be compact. Recognition systems with two types of parametric linear model are compared using a database of 20 persons. The results showed our system's robust recognition of faces with /spl plusmn/50 degree range of full 3D head rotation, while compressing the data by a factor of 20 and more.

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