Non-ideal class non-point light source quotient image for face relighting

Abstract

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.