The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR'04 Proceedings of the 2004 IEEE computer society 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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Quotient Image (QI) algorithm has been widely used in face recognition and re-rendering under varying illumination conditions. One of the inaccuracies of QI algorithm is the assumption of ''Ideal Class'', that all faces have the same surface normal (3D shape). However, in practice this assumption is often not true. To reduce the inaccuracy, the Non-Ideal Class Non-Point Light source QI (NIC-NPL-QI), which ignores the ''Ideal Class'' assumption, is developed in this paper for face relighting. Unlike that in the basic QI algorithm a fixed reference object for all test objects is used, in the NIC-NPL-QI algorithm a special reference object for each test object is constructed, so that the test and reference objects have similar illumination images, achieving the equal effect of ''Ideal Class'' assumption. In the proposed method, the wavelet algorithm is introduced to estimate an illumination image. Furthermore, the proposed NIC-NPL-QI algorithm can handle the harmonic light and shadows. Experiments on Extended Yale B and CMU-PIE databases show that NIC-NLP-QI algorithm obtains better quality in synthesizing face images as compared with state-of-the-art algorithms.