Recognising faces in unseen modes: a tensor based approach
MetadataShow full item record
This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost.
Showing items related by title, author, creator and subject.
Rana, Santu (2010)Machine based face recognition is an important area of research that has attracted significant attention over the past few decades. Recently, multilinear models of face images have gained prominence as an alternative ...
Li, Billy Y.L. (2013)One of the most important advantages of automatic human face recognition is its nonintrusiveness property. Face images can sometime be acquired without user's knowledge or explicit cooperation. However, face images acquired ...
Li, Billy; Liu, Wan-Quan; An, Senjian; Krishna, Aneesh (2012)In this paper we address the robust face recognition problem for color faces with large variations in pose, illumination and facial expression. A novel algorithm is proposed, namely the Multilinear Color Tensor Discriminant ...