Automated face detection and recognition is one of the most attentional branches of biometrics and it is also the one of the most active and challenging tasks for computer vision and pattern recognition .Over the past few years, some embedding methods have been proposed for feature extraction and dimensionality reduction in various machine learning and pattern classification tasks. Locality Preserving Projection (LPP) has been used in such applications as face recognition and image. In this paper, we propose some novel tensor embedding methods which, unlike previous methods, take data directly in the form of tensors of arbitrary order as input. These methods allow the relationships between dimensions of a tensor representation to be efficiently characterized. Extensive experiments show that our methods are not only more effective but also more efficient.
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