Illumination invariant extraction for face recognition using neighboring wavelet coefficients

• Published in: Pattern recognition 2012-04, Vol.45 (4), p.1299-1305
• Main Authors: Cao, X., Shen, W., Yu, L.G., Wang, Y.L., Yang, J.Y., Zhang, Z.W.
• Format: Article
• Language: English
• Subjects: Applied sciences; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Face recognition; High frequencies; Illumination; Illumination invariant; Image processing; Information, signal and communications theory; Invariants; Neighboring wavelet coefficients; NeighShrink denoise model; Pattern recognition; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Wavelet
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2011.09.010

The features of a face can change drastically as the illumination changes. In contrast to pose position and expression, illumination changes present a much greater challenge to face recognition. In this paper, we propose a novel wavelet based approach that considers the correlation of neighboring wavelet coefficients to extract an illumination invariant. This invariant represents the key facial structure needed for face recognition. Our method has better edge preserving ability in low frequency illumination fields and better useful information saving ability in high frequency fields using wavelet based NeighShrink denoise techniques. This method proposes different process approaches for training images and testing images since these images always have different illuminations. More importantly, by having different processes, a simple processing algorithm with low time complexity can be applied to the testing image. This leads to an easy application to real face recognition systems. Experimental results on Yale face database B and CMU PIE Face Database show that excellent recognition rates can be achieved by the proposed method. ► A novel approach considering the correlation of neighboring wavelet coefficients. ► Great edge preserving ability and useful information saving ability. ► Excellent recognition rate due to effective illumination invariant extraction. ► Different process approaches for training images and testing images. ► An easy application due to the simple processing algorithm for the testing image.