Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Smoothing: A General Tool for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
LCIS: a boundary hierarchy for detail-preserving contrast reduction
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Adaptive Smoothing via Contextual and Local Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
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
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Bayesian Tensor Approach for 3-D Face Modeling
IEEE Transactions on Circuits and Systems for Video Technology
Illumination invariant face recognition by non-local smoothing
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Efficient face recognition fusing dynamic morphological quotient image with local binary pattern
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Variable lighting face recognition using discrete wavelet transform
Pattern Recognition Letters
Illumination invariant eye detection in facial images based on the retinex theory
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Hi-index | 0.08 |
In this paper, we propose the Retinex method for illumination invariant face recognition developed on the basis of adaptive smoothing technology. By the well-known Retinex theory, illumination is generally estimated and normalized by smoothing the input image first and then dividing the estimate into the original input image. Therefore, performance mainly depends on how good the estimated illumination is. The proposed method estimates illumination by iteratively convolving the input image with a 3x3 smoothing mask weighted by a coefficient via combining two measures of the illumination discontinuity at each pixel. We address a couple of additional concepts, which are designed to be suitable especially for face images. One is the new conduction function for adaptive smoothing, and the other is the smoothing constraint for more accurate description of real environments. In this way, we can achieve an efficient illumination normalization in which face images with even strong shadows are normalized efficiently. The proposed method is evaluated based on Yale face database B, CMU PIE database and AR face database by applying PCA. The comparative results indicate that the proposed method present consistent and promising results even when images under harsh illumination conditions are used as a training set.