Abstract
In this paper, we investigate how to preprocess bad input face images for robust face recognition, under uneven illumination environments. Proposed filter combination shows nice performance under varying illumination, however, it can not provide the highest performance under normal illumination. We found that the performance of each preprocessing method for compensating illumination is highly affected by working illumination environment. Changing illumination poses a most challenging problem in face recognition. A previous research for illumination compensation has been investigated. This paper proposes a filter block for efficient face recognition. Since no priori knowledge of system working environment can be assumed. The proposed method can decide an optimal configuration of filter block by exploring the filter combination and the associated parameters to unknown illumination conditions. The illumination filter includes Retinex filter, end-in contrast stretching and histogram equalization filter. The proposed method has been tested to robust face recognition in varying illumination conditions (Inha DB, FERET DB). We made in illumination cluster using combined FART. Extensive experiment shows that the proposed system can achieve very encouraging performance in varying illumination environments. We furthermore show how this algorithm can be extended towards face recognition across illumination.
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Nam, M.Y., Rhee, P.K. (2005). On the Filter Combination for Efficient Image Preprocessing Under Uneven Illumination. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_74
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DOI: https://doi.org/10.1007/11552499_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
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