Skip to main content
Log in

Comparative analysis of simple facial features extractors

  • Survey Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

In the article a certain class of feature extractors for face recognition is presented. The extraction is based on simple approaches: image scaling with pixel concatenation into a feature vector, selection of a small number of points from the face area, face image’s spectrum, and finally pixel intensities histogram. The experiments performed on several facial image databases (BioID [4], ORL face database [27], FERET [30]) show that face recognition using this class of extractors is particularly efficient and fast, and can have straightforward implementations in software and hardware systems. They can also be used in fast face recognition system involving feature-integration, as well as a tool for similar faces retrieval in 2-tier systems (as initial processing, before exact face recognition).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Aeberhard, S., Coomans, D., de Vel, O.: Comparative analysis of pattern classifiers in a high dimensional setting. Pattern Recogn. 24, 1065–1077 (1994)

    Article  Google Scholar 

  2. Annadurai, S., Saradha, A.: Discrete cosine transform based face recognition using linear discriminant analysis. In: IJSIT Lecture Note of International Conference on Intelligent Knowledge Systems, vol. 1/1, August 2004 (2004)

  3. International Organization for Standardization.: Biometric Data Interchange Formats—Final Committee Draft http://isotc.iso.org/livelink/livelink/fetch/2000/2122/327993/2262372/2263034/2300191/JTC001-SC37-N-506.pdf?nodeid=3924597&vernum=0 (2004)

  4. The BioID Face Database. http://www.bioid.com/research/index.htm

  5. Emiris, I., Pan, V.: Applications of FFT. In: Atallah, M.J. (Ed) Handbook of Algorithms and Theory of Computation, chap. 17. CRC Press, Boca Raton (1999)

  6. Frigo, M., Johnson, S.G.: The Design and Implementation of FFTW3. Proc IEEE 93(2), 216–231. Invited paper, Special Issue on Program Generation, Optimization, and Platform Adaptation (2005)

  7. Hafed, Z.M., Lewine, M.D.: Face recognition using the Discrete Cosine Transform. Int. J. Comput. Vis. 43 3, 167–188 (2001)

    Article  MATH  Google Scholar 

  8. Huang, J., Yuen, P., Lai, J.H., Li, C-H.: Face recognition using local and global features. EURASIP J. Appl. Signal Process., pp. 530–541 (2004)

  9. Jianke, Z., Mang, I.V., Peng, U.M.: Face recognition using 2D DCT with PCA. In: Proceedings of the 4th Chinese Conference on Biometric Recognition (Sinobiometrics’03) at Beijing, Dec. 7–8, 2003 (2000)

  10. Jing, X.-Y., Tang, Y.-Y., Zhang, D.: A Fourier-LDA approach for image recognition. Pattern Recogn. 38 3, 453–457 (2005)

    Article  MATH  Google Scholar 

  11. Kompanets, L., Valchuk, T., and others.: Biometrical method of personal identification based on information about asymmetry and mimics (in Polish). In: Proceedings of 6th International Conference IT.FORUM SECURE 2002, Holiday Inn, Warszawa, NASK and MULTICOPY Press, vol. 2, pp. 31–40, 6–7 November 2002 (2002)

  12. Kukharev, G.: Biometrics systems. Methods and means of person identification. St. Petersburg (Russia), Politechnika, (in Russian), p. 240 (2001)

  13. Kukharev, G., Kuźmiński, A.: Biometric techniques. Part I. The Methods of Face Recognition (in Polish). Szczecin (Poland), Pracownia Poligraficzna WI PS, p. 310 (2003)

  14. Kukharev, G., Nowosielski, A.: Visitor identification—elaborating real time face recognition system. In: Short Communication Papers Proceedings of WSCG’2004., Plzen, Czech Republic, 157–164, 2–6 February 2004 (2004)

  15. Kukharev, G., Nowosielski, A.: Fast and efficient algorithm for face detection in colour images. Mach. Graph. Vis. 13(4), 377–399 (2004)

    Google Scholar 

  16. Kukharev, G., Forczmański, P.: Data dimensionality reduction for face recognition. Mach. Graph. Vis. 131/2 (2004)

  17. Kukharev, G., Kuźmiński, A., Nowosielski, A.: Structure and characteristics of face recognition systems. In: Computing, Multimedia and Intelligent Techniques. Special issue on Live Biometrics and Security. vol. 1, no. 1, pp. 111–124, June 2005 (2005)

  18. Kukharev, G., Kuźmiński, A.: Face Recognition Using the Histogram of Face Image. In: Computing, Multimedia and Intelligent Techniques. Special issue on Live Biometrics and Security, Politechnika Czestochowa, vol.1, no.1, pp. 61–71. June 2005 (2005)

  19. Kukharev, G., Forczmański, P.: Face recognition by means of two-dimensional direct linear discriminant analysis. In: Proceedings of the 8th International Conference PRIP’2005 Pattern Recognition and Information Processing, Republic of Belarus, Minsk, pp. 280–283 (2005)

  20. Kukharev, G., Masicz, Pa., Masicz, Pi.: Modified gradient method for face localization. In: Enhanced Methods in Computer Security, Biometrics and Artificial Intelligence Systems, pp. 165–176. Kluwer/Springer, Dordrecht/New York (2005)

  21. Kukharev, G., Mikłasz, M.: Face Retrieval from Large Database. Polish J. Environ. Stud. 15(4C), 111–114 (2006)

    Google Scholar 

  22. Lai, J.H., Yuen, P.C., Feng, G.C.: Face recognition using holistic Fourier invariant features. Pattern Recogn. 34(1), 95–109 (2001)

    Article  MATH  Google Scholar 

  23. Lanitis, A., Taylor, Ch.J., Cootes, T.F.: Automatic interpretation and coding of face images using flexible models. IEEE Trans. PAMI 17(7), 743–755 (1997)

    Google Scholar 

  24. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11(4), 467–476 (2002)

    Article  Google Scholar 

  25. Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Trans. PAMI 28(5), 725–737 (2006)

    Google Scholar 

  26. MATLAB 6.5. http://www.mathworks.com

  27. The ORL Database of Faces. http://'www.uk.research.att.com

  28. Pratt, W.K.: Digital Image Processing: PIKS Inside, 3rd edn, p. 738. Wiley, New York (2001)

  29. Pentland, A., Choudbury, T.: Face recognition for smart environments. IEEE Comput. pp. 50–55 (2000)

  30. Phillips, P., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image Vis. Comput. 16/5, 295–306 (1999)

    Google Scholar 

  31. Spies, H., Ricketts, I.: Face recognition in Fourier Space. In: Vision Interface’2000, Montreal, pp. 8–44 (2000)

  32. Stollnitz, E.J., DeRose, T.D., Salesin, D.H.: Wavelets for Computer Graphics: Theory and Applications. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

  33. Sun Microsystems, Inc. (2000) J2ME Building Blocks for Mobile Devices—White Paper on KVM and the Connected, Limited Device Configuration (CLDC), May 19, 2000

  34. Tan, X., Chen, S., Zhou, Z.-H., Zhan, F.: Face recognition from a single image per person: a survey. Pattern Recogn. 39, 1725–1745 (2006)

    Article  MATH  Google Scholar 

  35. Tistarelli, M., Grosso, E.: Active face recognition with a hybrid approach. Pattern Recogn. Lett. 18, 933–946 (2006)

    Article  Google Scholar 

  36. Tjahyadi, R., Liu, W., Venkatesh, S.: Application of the DCT energy histogram for face recognition. In: Proceedings of the 2nd International Conference on Information Technology for Application (ICITA 2004) (2004)

  37. Trang, K., Pelton, W., Hsu, P., Yossakda, N.: Sample integrated Fourier transform (SIFT) with high-performance ASIC implementation. 2001 In: MAPLD International Conference, Kossiakoff Conference Center, The Johns Hopkins University, Applied Physics Laboratory, Maryland, September 11–13 (2001)

  38. Vel, O., Aeberhard, S.: Line-based face recognition under varying pose. IEEE Trans. Pattern Anal. Mach. Intell. 21, 1081–1088 (1999)

    Article  Google Scholar 

  39. Wallace, G.K.: The JPEG still picture compression standard. Commun. ACM. 344 (1991)

  40. Yang, Z., Laaksonen, J.: Interactive retrieval in facial image database using self-organizing maps. In: Proceedings of IAPR Conference on Machine Vision Applications (MVA 2005), pp. 112–115. Tsukuba Science City, Japan, May 2005 (2005)

  41. Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2000)

    Article  Google Scholar 

  42. Zhang, Ch., Wang, J., Zhao, N., Zhang, D.: Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction. Pattern Recogn. 37(2), 325—336 (2004)

    Article  MATH  Google Scholar 

  43. Zhang, L.Z., Yang, Q., Bao, T., Vronay, D., Tang, X.: ImLooking: image-based face retrieval in online dating profile search. ACM SIG CHI, Montreal, April 22–27 (2006)

  44. Zhengjun, P., Adams, R., Bolouri, H.: Dimensionality reduction of face images using discrete cosine transforms for recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, South Carolina, June 13–15, 2000 (2000)

  45. Ziad, M., Levine, H.M.: Face recognition using the discrete Cosine transform. Int. J. Comput. Vis. 43(3), 167–188 (2001)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paweł Forczmański.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Forczmański, P., Kukharev, G. Comparative analysis of simple facial features extractors. J Real-Time Image Proc 1, 239–255 (2007). https://doi.org/10.1007/s11554-007-0030-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-007-0030-4

Keywords

Navigation