Journal of Zhejiang University SCIENCE C

, Volume 11, Issue 2, pp 79–91 | Cite as

Computer vision based eyewear selector

  • Oscar DénizEmail author
  • Modesto Castrillón
  • Javier Lorenzo
  • Luis Antón
  • Mario Hernandez
  • Gloria Bueno
Image Processing


The widespread availability of portable computing power and inexpensive digital cameras are opening up new possibilities for retailers in some markets. One example is in optical shops, where a number of systems exist that facilitate eyeglasses selection. These systems are now more necessary as the market is saturated with an increasingly complex array of lenses, frames, coatings, tints, photochromic and polarizing treatments, etc. Research challenges encompass Computer Vision, Multimedia and Human-Computer Interaction. Cost factors are also of importance for widespread product acceptance. This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

Key words

Face detection Eye detection Perceptual user interfaces Human-computer interaction 

CLC number



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  1. ABS, 2007. Smart Look. Available from [Accessed on July 30, 2007].
  2. Activisu, 2007. Activisu Expert. Available from [Accessed on July 30, 2007].
  3. Arkin, R.C., Balch, T., 1997. AuRA: Principles and practice in review. J. Exper. Theor. Artif. Intell., 9(2–3):175–189. [doi:10.1080/095281397147068]Google Scholar
  4. Azuma, R.T., 1997. A survey of augmented reality. Presence, 6:355–385.Google Scholar
  5. Battocchi, A., Pianesi, F., 2004. Dafex: Un Database di Espressioni Facciali Dinamiche. SLI-GSCPWorkshop Comunicazione Parlata e Manifestazione delle Emozioni, p.1–11.Google Scholar
  6. Bouguet, J., 1999. Pyramidal Implementation of the Lucas Kanade Feature Tracker. Technical Report, OpenCV Documents, Intel Corporation, Microprocessor Research Labs.Google Scholar
  7. Breazeal, C., 1998. A Motivational System for Regulating Human-Robot Interaction. AAAI/IAAI, p.54–61.Google Scholar
  8. Cañamero, D., 1997. Modeling Motivations and Emotions as a Basis for Intelligent Behavior. Proc. 1st Int. Conf. on Autonomous Agents, p.148–155. [doi:10.1145/267658.267688]Google Scholar
  9. Carl Zeiss Vision, 2007. Lens Frame Assistant. Available from [Accessed on July 30, 2007].
  10. Castrillón, M., Déniz, O., Hernández, M., Guerra, C., 2007. ENCARA2: real-time detection of multiple faces at different resolutions in video streams. J. Vis. Commun. Image Represent., 18(2):130–140. [doi:10.1016/j.jvcir.2006.11.004]CrossRefGoogle Scholar
  11. CBC Co., 2007. Camirror. Available from [Accessed on July 30, 2007].
  12. CyberImaging, 2007. CyberEyes. Available from [Accessed on July 30, 2007].
  13. Fiala, M., 2004. Artag, an Improved Marker System Based on Artoolkit. Technical Report, ERB-1111, NRC Canada.Google Scholar
  14. Gadanho, S.C., Hallam, J., 2001. Robot learning driven by emotions. Adapt. Behav., 9(1):42–64. [doi:10.1177/105971230200900102]CrossRefGoogle Scholar
  15. GlassyEyes, 2009. Trying Eyeglasses Online. GlassyEyes Blog. Available from [Accessed on Dec. 23, 2009].
  16. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W., 2001. Robust face detection using the Hausdorff distance. LNCS, 2091:90–95. [doi:10.1007/3-540-45344-X_14]Google Scholar
  17. Just, A., Rodriguez, Y., Marcel, S., 2006. Hand Posture Classification and Recognition Using the Modified Census Transform. Proc. Int. Conf. on Automatic Face and Gesture Recognition, p.351–356. [doi:10.1109/FGR.2006.62]Google Scholar
  18. Kölsch, M., Turk, M., 2004. Robust Hand Detection. Proc. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, p.614–619. [doi:10.1109/AFGR.2004.1301601]Google Scholar
  19. Kuttler, H., 2003. Seeing Is Believing. Using Virtual Tryons to Boost Premium Lens Sales. Available from [Accessed on Dec. 23, 2009].
  20. Lee, J.S., Jung, Y.Y., Kim, B.S., Ko, S.J., 2001. An advanced video camera system with robust AF, AE and AWB control. IEEE Trans. Consum. Electron., 47(3):694–699. [doi:10.1109/30.964165]CrossRefGoogle Scholar
  21. Lepetit, V., Vacchetti, L., Thalmann, D., Fua, P., 2003. Fully Automated and Stable Registration for Augmented Reality Applications. Proc. 2nd IEEE and ACM Int. Symp. on Mixed and Augmented Reality, p.93–102. [doi:10.1109/ISMAR.2003.1240692]Google Scholar
  22. Li, S., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H., 2002. Statistical learning of multi-view face detection. LNCS, 2353:67–81. [doi:10.1007/3-540-47979-1_5]Google Scholar
  23. Low, K.H., Leow, W.K., Ang, M.H.Jr., 2002. Integrated Planning and Control of Mobile Robot with Self-Organizing Neural Network. Proc. 18th IEEE Int. Conf. on Robotics and Automation, p.3870–3875. [doi:10.1109/ROBOT.2002.1014324]Google Scholar
  24. Lyu, M.R., King, I., Wong, T.T., Yau, E., Chan, P.W., 2005. ARCADE: Augmented Reality Computing Arena for Digital Entertainment. Proc. IEEE Aerospace Conf., p.1–9. [doi:10.1109/AERO.2005.1559626]Google Scholar
  25. Morgan, E., 2004. Dispensing’s New Wave. Eyecare Business. Available from [Accessed on Dec. 23, 2009].
  26. Nanda, H., Cutler, R., 2001. Practical Calibrations for a Real-Time Digital Omnidirectional Camera. Proc. Computer Vision and Pattern Recognition Conf., p.3578–3596.Google Scholar
  27. OfficeMate Software Systems, 2007. iPointVTO. Available from [Accessed on July 30, 2007].
  28. Picard, R., 1997. Affective Computing. MIT Press, Cambridge, MA.Google Scholar
  29. Practice, P., 2007. FrameCam. Available from [Accessed on July 30, 2007].
  30. Reimondo, A., 2007. OpenCV Swiki. Available from [Accessed on Dec. 23, 2009].
  31. Roberts, K., Threlfall, I., 2006. Modern dispensing tools. Options for customised spectacle wear. Optometry Today, 46(12):26–31.Google Scholar
  32. Rodenstock, 2007. ImpressionIST. Available from [Accessed on July 30, 2007].
  33. Sanchez-Nielsen, E., Anton-Canalis, L., Guerra-Artal, C., 2005. An autonomous and user-independent hand posture recognition system for vision-based interface tasks. LNCS, 4177:113–122. [doi:10.1007/11881216_13]Google Scholar
  34. Schneiderman, H., Kanade, T., 2000. A Statistical Method for 3D Object Detection Applied to Faces and Cars. IEEE Conf. on Computer Vision and Pattern Recognition, p.1746–1759.Google Scholar
  35. Stenger, B., Thayananthan, A., Torr, P., Cipolla, R., 2004. Hand pose estimation using hierarchical detection. LNCS, 3058:105–116. [doi:10.1007/b97917]Google Scholar
  36. Storring, M., Moeslund, T., Liu, Y., Granum, E., 2004. Computer Vision Based Gesture Recognition for an Augmented Reality Interface. 4th IASTED Int. Conf. on Visualization, Imaging, and Image Processing, p.766–771.Google Scholar
  37. Swain, M.J., Ballard, D.H., 1991. Color indexing. Int. J. Comput. Vis., 7(1):11–32. [doi:10.1007/BF00130487]CrossRefGoogle Scholar
  38. Velasquez, J., 1997. Modeling Emotions and Other Motivations in Synthetic Agents. Proc. AAAI Conf., p.10–15.Google Scholar
  39. Velasquez, J., 1998. Modeling Emotion-Based Decision Making. In: Canamero, D. (Ed.), Emotional and Intelligent: The Tangled Knot of Cognition. AAAI Press, Springer Netherlands, p.164–169.Google Scholar
  40. Viola, P., Jones, M.J., 2004. Robust real-time face detection. Int. J. Comput. Vis., 57(2):137–154. [doi:10.1023/B:VISI.0000013087.49260.fb]CrossRefGoogle Scholar
  41. Visionix, 2007. 3DiView 3D Virtual Try-on. Available from [Accessed on July 30, 2007].
  42. Wagner, S., Alefs, B., Picus, C., 2006. Framework for a Portable Gesture Interface. Proc. 7th Int. Conf. on Automatic Face and Gesture Recognition, p.275–280. [doi:10.1109/FGR.2006.54]Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and “Journal of Zhejiang University Science” Editorial Office 2010

Authors and Affiliations

  • Oscar Déniz
    • 1
    • 2
    Email author
  • Modesto Castrillón
    • 1
  • Javier Lorenzo
    • 1
  • Luis Antón
    • 1
  • Mario Hernandez
    • 1
  • Gloria Bueno
    • 2
  1. 1.Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en IngenieríaUniversidad de Las Palmas de Gran Canaria, Edificio Central del Parque Científico-TecnológicoLas PalmasSpain
  2. 2.E.T.S. Ingenieros IndustrialesUniversidad de Castilla-La Mancha Campus UniversitarioCiudad RealSpain

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