Performance of a contemporary two-dimensional facerecognition system has not been satisfied due to the variation in lighting. As a result, many works of solving illumination variation in face recognition have been carried out in past decades. Among them, the Illumination-Reflectance model is one of the generic models that is used to separate the individual reflectance and illumination components of an object. The illumination component can be removed by means of image-processing techniques to regain an intrinsic face feature, which is depicted by the reflectance component. We present a wavelet-based illumination invariant algorithm as a preprocessing technique for face recognition. On the basis of the multiresolution nature of wavelet analysis, we decompose both illumination and reflectance components from a face image in a systematic way. The illumination component wherein resides in the lowspatial- frequency subband can be eliminated efficiently. This technique works out very advantageously for achieving higher recognition performance on YaleB, CMU PIE, and FRGC face databases.
Bibliographical noteFunding Information:
Portions of the research in this paper used the YaleB, CMU PIE, and FRGC face databases. The authors thank everyone involved in collecting these data and also thank http:// torch3vision.idiap.ch/ for anisotropic smoothing (Gross method) algorithms. This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University (Grant No. R112002105080020 ).
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Computer Science Applications
- Electrical and Electronic Engineering