Optical Review

, Volume 24, Issue 4, pp 517–528 | Cite as

Principal component analysis for surface reflection components and structure in facial images and synthesis of facial images for various ages

  • Misa Hirose
  • Saori Toyota
  • Nobutoshi Ojima
  • Keiko Ogawa-Ochiai
  • Norimichi Tsumura
Regular Paper

Abstract

In this paper, principal component analysis is applied to the distribution of pigmentation, surface reflectance, and landmarks in whole facial images to obtain feature values. The relationship between the obtained feature vectors and the age of the face is then estimated by multiple regression analysis so that facial images can be modulated for woman aged 10–70. In a previous study, we analyzed only the distribution of pigmentation, and the reproduced images appeared to be younger than the apparent age of the initial images. We believe that this happened because we did not modulate the facial structures and detailed surfaces, such as wrinkles. By considering landmarks and surface reflectance over the entire face, we were able to analyze the variation in the distributions of facial structures and fine asperity, and pigmentation. As a result, our method is able to appropriately modulate the appearance of a face so that it appears to be the correct age.

Keywords

Principal component analysis Surface reflectance Wrinkle Facial structure Facial appearance 

References

  1. 1.
    Scherbaum, K., Ritschel, T., et al.: Computer-suggested facial makeup. Comput. Graphics Forum 30(2), 485–492 (2011)CrossRefGoogle Scholar
  2. 2.
    Guo, D., Sim, T.: Digital face makeup by example. In: Computer Vision and Pattern Recognition, pp. 73–79 (2009)Google Scholar
  3. 3.
    Lantis, A., et al.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)CrossRefGoogle Scholar
  4. 4.
    Chalothorn, L., Akiba, M.,et al.: Extracted facial feature of racial closely related faces. In: Proceedings of SPIE7546, Second International Conference on Digital Image Processing, 75461R (2010)Google Scholar
  5. 5.
    Tsumura, N., Haneishi, H., Miyake, Y.: Independent component analysis of skin color image. J. Optic. Soc. Am. A 16(9), 2169–2176 (1999)ADSCrossRefGoogle Scholar
  6. 6.
    Tsumura, N., Ojima, N., et al.: Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. ACM Trans. Graph. 22, 770–779 (2003)CrossRefGoogle Scholar
  7. 7.
    Tsumura, N., Nakaguchi, T., Ojima, N., Takase, K., Okaguchi, S., Hori, K., Miyake, Yoichi: Image-based control of skin melanin texture. Appl. Opt. 45(25), 6626–6633 (2006)ADSCrossRefGoogle Scholar
  8. 8.
    Toyota, S., Fujiwara, I., Hirose, M., Ojima, N., Ogawa-Ochiai, K., Tsumura, N.: Principal component analysis for the whole facial image with pigmentation separation and application to the prediction of facial images at various ages. J. Imaging Sci. Technol. 58(2), 020503-1–020503-11 (2014)CrossRefGoogle Scholar
  9. 9.
    Mukaida, S., Kamachi, M., et al.: Facial image synthesis system: FUTON-evaluation as tools for cognitive research on face processing. IEICE Trans Fundam Electron Commun. Comput. Sci. J 85A(10), 1126–1137 (2002)Google Scholar

Copyright information

© The Optical Society of Japan 2017

Authors and Affiliations

  1. 1.Graduate School of Advanced Integration ScienceChiba UniversityChibaJapan
  2. 2.Global R&D Beauty CreationKao CorporationTokyoJapan
  3. 3.Clinic of Japanese-Oriental (Kampo) Medicine, Department of Otorhinolaryngology & Head and Neck SurgeryKanazawa University HospitalKanazawaJapan

Personalised recommendations