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
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
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Binary Patterns as an Image Preprocessing for Face Authentication
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Journal of Cognitive Neuroscience
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
Hi-index | 12.05 |
Poor illumination condition is recognized as one of the major problem in contemporary two-dimensional (2D) face verification system. It causes large variation in facial images and degrades the performance of the system. Many works of resolving illumination variation in face verification have been reported in the past decades. In this paper, a facial image illumination invariant technique is devised based on the fusion of wavelet analysis and local binary patterns. Particularly, illumination-reflectance model is used to detach illumination and reflectance components with multi-resolution nature of wavelet analysis. The illumination component that resides in low spatial-frequency wavelet subband is first rid off efficiently. The reflectance components that reside in high and middle spatial-frequency wavelet subbands are enhanced with local binary patterns histogram. Finally, two processed images are fused through wavelet image fusion. This technique works out promisingly in achieving better recognition results on YaleB, CMU PIE and FRGC face databases in comparison with existing illumination invariant techniques.