Abstract
The proposed method for low-dimensional illumination space representation (LDISR) of human faces can not only synthesize a virtual face image when given lighting conditions but also estimate lighting conditions when given a face image. The LDISR is based on the observation that 9 basis point light sources can represent almost arbitrary lighting conditions for face recognition application and different human faces have a similar LDISR. The principal component analysis (PGA) and the nearest neighbor clustering method are adopted to obtain the 9 basis point light sources. The 9 basis images under the 9 basis point light sources are then used to construct an LDISR which can represent almost all face images under arbitrary lighting conditions. Illumination ratio image (IRI) is employed to generate virtual face images under different illuminations. The LDISR obtained from face images of one person can be used for other people. Experimental results on image reconstruction and face recognition indicate the efficiency of LDISR.
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