A subspace model-based approach to face relighting under unknown lighting and poses

Cited 39 time in webofscience Cited 0 time in scopus
  • Hit : 117
  • Download : 0
We present a new approach to face relighting by jointly estimating the pose, reflectance functions, and lighting from as few as one image of a face. Upon such estimation, we can synthesize the face image under any prescribed new lighting condition. In contrast to commonly used face shape models or shape-dependent models, we neither recover nor assume the 3-D face shape during the estimation process. Instead, we train a pose- and pixel-dependent subspace model of the reflectance function using a face database that contains samples of pose and illumination for a large number of individuals (e.g., the CMU PIE database and the Yale database). Using this subspace model, we can estimate the pose, the reflectance functions, and the lighting condition of any given face image. Our approach lends itself to practical applications thanks to many desirable properties, including the preservation of the non-Lambertian skin reflectance properties and facial hair, as well as reproduction of various shadows on the face. Extensive experiments show that, compared to recent representative face relighting techniques, our method successfully produces better results, in terms of subjective and objective quality, without reconstructing a 3-D shape.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2008-08
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON IMAGE PROCESSING, v.17, no.8, pp.1331 - 1341

ISSN
1057-7149
DOI
10.1109/TIP.2008.925390
URI
http://hdl.handle.net/10203/297185
Appears in Collection
AI-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 39 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0