International Journal of Computer Vision

, Volume 19, Issue 2, pp 129–146 | Cite as

Locating human faces in photographs

  • Venu Govindaraju


The human face is an object that is easily located in complex scenes by infants and adults alike. Yet the development of an automated system to perform this task is extremely challenging. An attempt to solve this problem raises two important issues in object location. First, natural objects such as human faces tend to have boundaries which are not exactly described by analytical functions. Second, the object of interest (face) could occur in a scene in various sizes, thus requiring the use of scale independent techniques which can detect instances of the object at all scales.

Although, the task of identifying a well-framed face (as one of a set of labeled faces) has been well researched, the task of locating a face in a natural scene is relatively unexplored. We present a computational theory for locating human faces in scenes with certain constraints. The theory will be validated by experiments confined to instances where people's faces are the primary subject of the scene, occlusion is minimal, and the faces contrast well against the background.


Image Processing Artificial Intelligence Analytical Function Computer Vision Computer Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Arnold, E.C. 1969. Modern Newspaper Design. Harper and Row: New York, NY.Google Scholar
  2. Augusteijn, M.F. and Skujca, T.L. 1993. Identification of human faces through texture-based feature recognition and neural network technology. 1993 IEEE Conference on Neural Networks, 392–398.Google Scholar
  3. Beus, H.L. and Tiu, S.S. 1987. An improved corner detection algorithm based on chain-coded plane curves. Pattern Recognition, 20:291–296.Google Scholar
  4. Bromley, L.K. 1977. Computer-aided processing techniques for usage in real-time image evaluation. Master's Thesis, Univesity of Houston.Google Scholar
  5. Cheng, J.K. and Huang, T.S. 1982. Recognition of curvilinear objects by matching relational structures. In Proc. Pattern Recognition and Image Processing, pp. 343–348.Google Scholar
  6. Ciba, N. and Nishizeki, T. 1985. Abrocity and subgraph listing algorithms. SIAM J. of Computing, 14(1):210–223.Google Scholar
  7. Farkas, L.G. and Munro, I.R. 1987. Anthropometric Facial Proportions in Medicine, Charles C. Thomas: Springfield, USA.Google Scholar
  8. Fischler, M.A. and Elschlager, R.A. 1973. The representation and matching of pictorial structures. IEEE Transactions on Computer, c-22(1).Google Scholar
  9. Freeman, H. and Davis, L.S. 1977. A corner finding algorithm for chain-coded curves. IEEE Trans. Comput. 26:297–303.Google Scholar
  10. Govindaraju, V., Srihari, S.N., and Sher, D.B. 1992. A computational model for face location based on cognitive principles. In Proc. of AAAI-92, San Jose, CA, pp. 350–355.Google Scholar
  11. Govindaraju, V., Sher, D.B., and Srihari, S.N. 1990. A computational model for face location. In Proc. of IEEE-CS Third Int. Conference on Computer Vision, Osaka, Japan, pp. 718–721.Google Scholar
  12. Govindaraju, V. and Srihari, R.K. 1990. Automatic face recognition in news photo database. Advanced Imaging, 5(11):22–26.Google Scholar
  13. Govindaraju, V., Sher, D.B., Srihari, N., and Srihari, S.N. 1989. Locating human faces in newspaper photographs. In Proc. of IEEE-CS Conf. Computer Vision and Pattern Recognition, San Diego, CA, pp. 278–285.Google Scholar
  14. Govindaraju, V., Lam, S., Niyogi, D., Sher, D.B., Srihari, R., Srihari, S.N., and Lam, D. Newspaper Images Understanding. Lecture Notes in Artificial Intelligence, Vol. 444, pp. 375–386, J. Siekmann (Ed.), Springer Verlag, New York, NY.Google Scholar
  15. Kanade, T. 1973. Picture Processing System by Computer Complex and Recognition of Human Faces. Department of Information Science, Kyoto University.Google Scholar
  16. Lambert, L.C. 1987. Evaluation and enhancement of the AFIT autonomous face recognition machine. Master's Thesis. Air Force Institute of Technology.Google Scholar
  17. Marr, D. and Hildreth, E. 1980. Theroy of edge detection. Proc. of the Royal Society of London, 207:187–217.Google Scholar
  18. Medioni, G. and Yasumoto, Y. 1987. Corner detection and curve representation using cubic B-splines. CVGIP 39:267–278.Google Scholar
  19. Pavlidis, T. 1982. Graphics and Image Processing. Computer Science Press, 1803. Research Boulevard, Rockville, MD.Google Scholar
  20. Sakai, T., Nagao, M., and Fujibayashi, S. 1969. Line extraction and pattern detection in a photograph. Pattern Recognition, 2:233–248.Google Scholar
  21. Seitz, P. and Lang, G.K. 1991. Using local orientation and heirarchical spatial feature matching for the robust recognition of objects. VCIP, Proceeding of SPIE, 252–259.Google Scholar
  22. Smith, E.J. 1986. Development of an autonomous face recognition machine. Master's Thesis, Air Force Institute of Technology.Google Scholar
  23. Srihari, R.K. 1991. Extracting Visual Information From Text: Using Captions to Label Faces in Newspaper Photographs. Ph.D. Thesis, State University of New York at Buffalo.Google Scholar
  24. Turk, M. and Pentland, A. 1991. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1): 71–86.Google Scholar
  25. Vailliant, R., Monrocq, C., and Le, Cun, Y. 1993. Original approach for the location of objects in images. 3rd Internation Conference on Artificial Neural Networks, 372:26–29.Google Scholar
  26. Vailliant, R., Monrocq, C., and Le, Cun, Y. 1993. Location of faces in images. Riview Technique Thomson, 25(1):23–40.Google Scholar
  27. Walters, D. 1987. Spur removal in ϱ-space. In Personal Communication.Google Scholar
  28. Yuille, A., Cohen, D., and Hallinan, P. 1988. Facial feature extraction by deformable templates. Technical Report 88-2, Harvard Robotics Laboratory.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Venu Govindaraju
    • 1
  1. 1.Center of Excellence for Document Analysis and Recognition (CEDAR), Department of Computer ScienceState University of New York at BuffaloBuffalo

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