Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Properties of a Center/Surround Retinex: Part 1. Signal Processing Design
Properties of a Center/Surround Retinex: Part 1. Signal Processing Design
Local Binary Patterns as an Image Preprocessing for Face Authentication
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Wavelet Based Illumination Invariant Preprocessing in Face Recognition
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 3 - Volume 03
An image preprocessing algorithm for illumination invariant face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
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Contemporary 2D face recognition is still a challenging work, especially when lighting varies. Thus, many works of resolving illumination variation in face recognition have been proposed, in the past decades. In this paper, we proposed Wavelet Local Binary Patterns Histogram Specification as a preprocessing technique for illuminated face recognition. Based on wavelet analysis, an illuminated facial image is decomposed into illumination and reflectance components. The illumination component that resides in the low spatial-frequency subband is first removed. Next, the reflectance component that resides in the high and middle spatial-frequency subband is then enhanced with local binary pattern histogram. This technique is promising in achieving better recognition performance on YaleB and CMU PIE face databases in comparison to the results that achieved by existing illumination invariant techniques.