Learning mappings for face synthesis from near infrared to visual light images

This paper deals with a new problem in face recognition research, in which the enrollment and query face samples are captured under different lighting conditions. In our case, the enrollment samples are visual light (VIS) images, whereas the query samples are taken under near infrared (NIR) condition. It is very difficult to directly match the face samples captured under these two lighting conditions due to their different visual appearances. In this paper, we propose a novel method for synthesizing VIS images from NIR images based on learning the mappings between images of different spectra (i.e., NIR and VIS). In our approach, we reduce the inter-spectral differences significantly, thus allowing effective matching between faces taken under different imaging conditions. Face recognition experiments clearly show the efficacy of the proposed approach.

[1]  I. Meglinski,et al.  Quantitative assessment of skin layers absorption and skin reflectance spectra simulation in the visible and near-infrared spectral regions. , 2002, Physiological measurement.

[2]  Cheng Lu,et al.  On the removal of shadows from images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  R. Doornbos,et al.  The determination of in vivo human tissue optical properties and absolute chromophore concentrations using spatially resolved steady-state diffuse reflectance spectroscopy. , 1999, Physics in medicine and biology.

[4]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[5]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Xiaogang Wang,et al.  Face sketch synthesis and recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  David Salesin,et al.  Image Analogies , 2001, SIGGRAPH.

[11]  Shengcai Liao,et al.  Illumination Invariant Face Recognition Using Near-Infrared Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Hanqing Lu,et al.  A nonlinear approach for face sketch synthesis and recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[14]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[15]  Ran He,et al.  Face shape recovery from a single image using CCA mapping between tensor spaces , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Dahua Lin,et al.  Inter-modality Face Recognition , 2006, ECCV.

[19]  Dong Yi,et al.  Face Matching Between Near Infrared and Visible Light Images , 2007, ICB.