Face Detection Using Sketch Operators and Vertical Symmetry

  • Hyun Joo So
  • Mi Hye Kim
  • Yun Su Chung
  • Nam Chul Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4027)


In this paper, we propose an algorithm for detecting a face in a target image using sketch operators and vertical facial symmetry (VFS). The former are operators which effectively reflect perceptual characteristics of human visual system to compute sketchiness of pixels and the latter means the bilateral symmetry which a face shows about its central longitudinal axis. In the proposed algorithm, horizontal and vertical sketch images are first obtained from a target image by using a directional BDIP (block difference inverse probabilities) operator which is modified from the BDIP operator. The pair of sketch images is next transformed into a generalized symmetry magnitude (GSM) image by the generalized symmetry transform (GST). From the GSM image, face candidates are then extracted which are quadrangular regions enclosing the triangles that satisfy eyes-mouth triangle (EMT) conditions and VFS. The sketch image for each candidate is obtained by the BDIP operator and classified into a face or nonface by the Bayesian classifier. Among the face candidates classified into faces, one with the largest VFS becomes the output where the EMT gives the location of two eyes and a mouth of a target face. If the procedure detects no face, then it is executed again after illumination compensation on the target image. Experimental results for 1,000 320x240 target images of various backgrounds and circumstances show that the proposed method yields about 97% detection rate and takes a time less than 0.25 second per target image.


Principle Component Analysis Target Image Face Detection Feature Enhancement Sobel Operator 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hielmås, E.: Face Detection: A Survey. Computer Vision and Image Understanding 83(3), 236–274 (2001)CrossRefGoogle Scholar
  2. 2.
    Reisfeld, D., Wolfson, H., Yeshurun, Y.: Context Free Attentional Operators: The Generalized Symmetry Transform. Int. J. of Computer Vision 14(3), 119–130 (1995)CrossRefGoogle Scholar
  3. 3.
    Maio, D., Maltoni, D.: Real-time Face Location on Gray-Scale Static Images. Pattern Recognition 33(9), 1525–1539 (2000)CrossRefGoogle Scholar
  4. 4.
    Liu, C.: A Bayesian Discriminating Features Method For Face Detection. IEEE Trans. on PAMI 25(6), 725–740 (2003)Google Scholar
  5. 5.
    Li, Y., Gong, S., Sherrah, J., Liddell, H.: Support Vector Machine Based Multi-View Face Detection and Recognition. Image and Vision Computing 22(5), 413–427 (2004)CrossRefGoogle Scholar
  6. 6.
    Huang, L.L., Shimizu, A., Hagihara, Y., Kobatake, H.: Gradient Feature Extraction for Classification-Based Face Detection. Pattern Recognition 36(11), 2501–2511 (2003)CrossRefGoogle Scholar
  7. 7.
    Gundimada, S., Asari, V.: Face Detection Technique Based on Rotation Invariant Wavelet Features. In: Int. Conf. on Information Technology: Coding and Computing, vol. 2, pp. 157–158 (2004)Google Scholar
  8. 8.
    Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Detection. In: Proc. IEEE Conf. on. Computer Vision and Pattern Recognition, pp. 130–136 (1997)Google Scholar
  9. 9.
    Feng, Y.J., Shi, P.F.: Face Detection Based on Kernel Fisher Discriminant Analysis. In: Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 381–384 (2004)Google Scholar
  10. 10.
    Garcia, C., Delakis, M.: Convolutional Face Finder: a Neural Architecture for Fast and Robust Face Detection. IEEE Trans. on PAMI, 26(11), 1408–1423 (2004)Google Scholar
  11. 11.
    Chun, Y.D., Seo, S.Y., Kim, N.C.: Image Retrieval Using BDIP and BVLC Moments. IEEE Trans. on CSVT 13(9), 951–957 (2003)Google Scholar
  12. 12.
    Katahara, S., Aoki, M.: Face Parts Extraction Windows Based on Bilateral Symmetry of Gradient Direction. In: Proc. Int. Conf. Computer Analysis of Image Patterns, pp. 489–497 (1999)Google Scholar
  13. 13.
    Tsalakanidou, F., Malassiotis, S., Strintzis, M.G.: Exploitation of 3D Images for Face Authentication Under Pose and Illumination Variations. In: IEEE Int. Symposium on 3D Data Processing, Visualization and Transmission, pp. 50–57 (2004)Google Scholar
  14. 14.
    Malassiotis, S., Strintzis, M.G.: Pose and Illumination Compensation for 3D Face Recognition. In: Proc. IEEE ICIP, vol. 1, pp. 91–94 (2004)Google Scholar
  15. 15.
    Kimmel, R., Elad, M., Shaked, D., Keshet, R., Sobel, I.: A Variational Framework for Retinex. Int. J. Computer Vision 52(1), 7–23 (2003)MATHCrossRefGoogle Scholar
  16. 16.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison Wesley, Reading (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyun Joo So
    • 1
  • Mi Hye Kim
    • 1
  • Yun Su Chung
    • 2
  • Nam Chul Kim
    • 1
  1. 1.Laboratory for Visual Communications, School of Electrical Engineering and Computer ScienceKyungpook National UniversityDaeguKorea
  2. 2.Biometric Chipset Technology Research Team, Electronics and Telecommunications, Research InstituteDaejeonKorea

Personalised recommendations