Recovering Facial Intrinsic Images from a Single Input
According to Barrow and Tenenbaum’s theory, an image can be decomposed into two images: a reflectance image and an illumination image. This midlevel description of images attracts more and more attentions recently owing to its application in computer vision, i.e. facial image processing and face recognition. However, due to its ill-posed characteristics, this decomposition remains difficult. In this paper, we concentrate on a slightly easier problem: given a simple frontal facial image and a learned near infrared image, could we recover its reflectance image? Experiments show that it is feasible and promising. Based on extensive study on hyperspectral images, skin color model and Quotient Image, we proposed a method to derive reflectance images through division operations. That is to divide visual frontal face images by learned near infrared images which are generated by super-resolution in tensor space. With the operation on grey distribution of frontal facial images, the results after division can represent the reflectance of skin, rarely bearing any illumination information. Experimental results show that our method is reasonable and promising in image synthesis, processing and face recognition.
KeywordsIntrinsic images near infrared super-resolution Tensorfaces Multi-Spectral Quotient Image
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- 5.Blanz, V., Vetter, T.: Morphable Model for the Synthesis of 3D Faces. In: Proc. ACM SIGGRAPH (1999)Google Scholar
- 7.Barrow, H.G., Tenenbaum, J.M.: Recovering Intrinsic Scene Characteristics from Images. Computer Vision System (1978)Google Scholar
- 8.Weiss, Y.: Deriving Intrinsic Images from Image Sequences. In: Proc. of IEEE ICCV (2001)Google Scholar
- 10.Tuchin, V.: Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis. SPIE Press, Bellingham (2000)Google Scholar
- 13.Angelopoulou, E., Molana, R., Daniilidis, K.: Multispectral Skin Color for Modeling. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (2001)Google Scholar
- 15.Jia, K., Gong, S.: Multi-modal tensor face for simultaneous super-resolution and recognition. In: Proc. IEEE Int. Conf. Computer Vision (2005)Google Scholar
- 16.Wang, H., Li, S.Z., Wang, Y.: Face Recognition Under Varying Lighting Conditions Using Self Quotient Image. In: International Conference on FGR, pp. 819–824 (2004)Google Scholar