Retinex-based illumination normalization using class-based illumination subspace for robust face recognition

Recent illumination normalization (IN) methods first decompose a face image into a reflectance (R)-image having a lighting-invariant characteristic and an illuminance (I)-image including shading and shadowing effects. An illumination-normalized I-image is then obtained by eliminating the lighting-dependent image variations (LDIV) from the I-image. Finally, the normalized I-and R-images are recombined for face recognition (FR). However, the decomposed-reflectance is often contaminated with the lighting effects. Moreover, the lighting normalization tends to remove the valuable discriminant information in the I-image. To address these problems, we employ the local edge-preserving filter to generate the R-image whereby the lighting-invariant information is well preserved. In addition, we propose a subspace-based IN method that can retain the large facial-structure in the I-image. To construct the proposed subspace, we calculate the LDIV within the same class of people from the training database of face images. Then, we apply the singular value decomposition to the calculated LDIV to obtain the basis images of the subspace. By projecting the I-image onto these basis images, we can effectively extract and eliminate the LDIV from the I-image without discarding the discriminant information. Experimental results confirm that FR with the proposed method outperforms that with existing IN methods under varying lighting conditions.