The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation of Albedo for Illumination-Invariant Matching and Shape Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Relighting from a Single Image under Arbitrary Unknown Lighting Conditions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
FRVT 2006 and ICE 2006 Large-Scale Experimental Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face image relighting using locally constrained global optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Illumination normalization of facial images by reversing the process of image formation
Machine Vision and Applications
Face illumination transfer through edge-preserving filters
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses
IEEE Transactions on Image Processing
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3D2D face recognition is beginning to gain attention from the research community. It takes advantage of 3D facial geometry to normalize the head pose and registers it into a canonical 2D space. In this paper, we present a novel illumination normalization approach for 3D2D face recognition which does not require any training or prior knowledge on the type, number, and direction of the lighting sources. Estimated using an image-specific filtering technique in the frequency domain, a self-lighting ratio is employed to suppress illumination differences. Experimental results on the UHDB11 and FRGC databases indicate that the proposed approach improves the performance significantly for face images with large illumination variations.