Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spectrum †

During the night or in poorly lit areas, thermal cameras are a better choice instead of normal cameras for security surveillance because they do not rely on illumination. A thermal camera is able to detect a person within its view, but identification from only thermal information is not an easy task. The purpose of this paper is to reconstruct the face image of a person from the thermal spectrum to the visible spectrum. After the reconstruction, further image processing can be employed, including identification/recognition. Concretely, we propose a two-step thermal-to-visible-spectrum reconstruction method based on Canonical Correlation Analysis (CCA). The reconstruction is done by utilizing the relationship between images in both thermal infrared and visible spectra obtained by CCA. The whole image is processed in the first step while the second step processes patches in an image. Results show that the proposed method gives satisfying results with the two-step approach and outperforms comparative methods in both quality and recognition evaluations.

[1]  Takeo Kanade,et al.  Hallucinating faces , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[2]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[3]  Chun Qi,et al.  Hallucinating Faces: Global Linear Modal Based Super-Resolution and Position Based Residue Compensation , 2009, ICIAP.

[4]  H. Hotelling Relations Between Two Sets of Variates , 1936 .

[5]  Chao Zhang,et al.  Hallucinating faces from thermal infrared images , 2008, 2008 15th IEEE International Conference on Image Processing.

[6]  Yunhong Wang,et al.  Face synthesis from near-infrared to visual light via sparse representation , 2011, 2011 International Joint Conference on Biometrics (IJCB).

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

[8]  Harry Shum,et al.  A two-step approach to hallucinating faces: global parametric model and local nonparametric model , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[10]  Qiuping Xu Canonical correlation Analysis , 2014 .

[11]  Matti Pietikäinen,et al.  Learning mappings for face synthesis from near infrared to visual light images , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Takahiro Okabe,et al.  Converting Near Infrared Facial Images to Visible Light Images using Skin Pigment Model , 2013, MVA.

[13]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[14]  Chao Zhang,et al.  Converting Thermal Infrared Face Images into Normal Gray-Level Images , 2007, ACCV.

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[16]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.