Illumination variation is a challenging issue in face recognition. In many conventional approaches the low-frequency coefficients are usually discarded in order to compensate the illumination variations, and hence degrade the visual quality. To deal with these problems, an adaptive normalisation-based method is proposed in this study. Each image is normalised according to its lighting attribute by mapping the low-frequency components to the normal condition instead of discarding them by applying a novel statistical concept called light mapping matrix. The method preserves the low-frequency facial features, maximising the intra-individual correlation and improves the visual quality of face images in different lighting conditions.

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