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Face images under wild atmosphere suffer from the changes of heterogeneous factors such as camera view, illumination, expression, etc. Tensor analysis brings a trail of analyzing the influence of heterogeneous factors on facial variety. However, the Tensor Face model develops an obstacle in symbolizing the non-linearity of view subspace. In this paper, to snap this limitation, the authors furnish a adaptive multi-resolution based technique which contains a view manifold-based Tensor Face in which the latent view manifold preserves the local distances in the multi-view face space. Moreover, a Kernelized Tensor Face for multi-view face recognition is named to freeze the structure of the latent manifold in the image space.
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