Laplacian Smoothing Transform (LST) for Face Recognition 1.0

Subspace learning based face recognition methods have attracted many researchersd-deOao interests in recent years. In thispaper, a novel Laplacian Smoothing Transform (LST) is proposed to transform an image into a sequence, by which low frequencyfeatures of an image can be easily extracted for a subspace learning method for face recognition. Generally, the LST is able to be used as a pre-processing method of a learning method for a face recognition. Extensive experimental results show that the LST method performs better than other preprocessing methods, such as discrete cosine transform, principal component analysis and discrete wavelet transform, on ORL, Yale and PIE face databases. Under the leave one out strategy, the best performance on the ORL and Yale face databases is 99:25% and 99:4%, however, In this paper, we improve both to 100% with a fast linear feature extraction method forthe first time.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Component,  Cosine,  Discrete,  Experimental,  Extensive,  Performs,  Preprocessing,  Principal
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