Skip to main content
Log in

Face sketch recognition: an application of Z-numbers

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

To simulate the capability of human decision making with rationality on a machine is a quite difficult task. In the seminal line the automation of sketching of face involves computation of words and propositions based on human perceptions. It is this challenge that motivated us to apply the concept of ‘Sketching With Words’ or SWW. Moreover, the computerization of sketching of face is augmented by the involvement of uncertainty, in perception of human, e.g. ‘I am not quite sure, nose of criminal was fairly small’. A perception based natural language statements may be represented by a set of tuples. Simple examples of Z-valuations are: (he has small eyes, closed to 2.8 cm, very likely), (forehead of criminal, small, not sure), (his nose, about 2 in., quite sure). Since ‘Z-numbers’ provides a foundation for a theory that can simulate a wide variety of rational decision made by humans, without any measurements and computations and might be made by machines. Hence, ‘Z-numbers’ are used as a tool of automation of such type of task, where the decision making is influenced by perception, experience, and mental status of human being. Various geometric objects based on fuzzy geometry are used for drawing different parts of face. These fuzzy objects are drawn by using SWW technique. Since computation of SWW with Z-numbers may provide ability to build model of real life. Hence these two concepts are used in proposed work to draw the sketch of facial features of miscreant on the basis of perceptions of eyewitness.

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

Similar content being viewed by others

References

  1. Rahman A, Beg MMS (2014) Face sketch recognition using sketching with words. Springer J Mach Learn Cybern 6(4):597–605. https://doi.org/10.1007/s13042-014-0256-y

    Article  Google Scholar 

  2. Rahman A, Beg MMS (2014) Investigation of OWA operator weights for estimating fuzzy validity of geometric shapes. In: Jamshidi M, Kreinovich V, Kacprzyk J (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer. Berlin, Heidelberg, pp 15–24

    Chapter  Google Scholar 

  3. Rahman A, Beg MMS (2014) Estimation of f-validity of geometrical objects with OWA operator weights, an extended paper. In: Special issue of BIJIT on fuzzy logic of BVICAM’s International Journal of Information Technology, (BJIT) ISSN 0973–5658, vol 6. pp 663–665

  4. Rahman A, Beg MMS (2013) Investigation of OWA operator weights for estimating f-validity of geometrical shapes. In: Third annual world conference on soft computing, (WCSC-2013) San Antonio, Texas, USA

  5. Rahman A, Beg MMS (2013) Estimation of f-validity of geometrical objects with OWA operator weights. In: Fuzz-2013 IEEE international conference on fuzzy systems on July 7–10; Hyderabad India

  6. Rahman A, Beg MMS (2014) Towards aggregation in fuzzy objects. In: Proceeding of 5th international conference on computer and communication technology ICCCT-2014, September 26–28 Allahabad India, pp 373–380

  7. Imran BM, Beg MMS (2010) Elements of sketching with words. In: Proceedings of IEEE, international conference on granular computing, (GrC2010); San Jose, California, USA, August 2010, pp 241–246

  8. Zadeh LA (2007) From fuzzy logic to extended fuzzy logic—the concept of f-validity and the impossibility principle, plenary session, FUZZ-IEEE 2007; Imperial College, London, UK

  9. Zadeh LA (1999) From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. IEEE Trans Circuits Syst I 45:105–119

    Article  MathSciNet  MATH  Google Scholar 

  10. Zadeh LA (2011) A note on Z-numbers. J Inf Sci 181(14):2923–2932

    Article  MATH  Google Scholar 

  11. Imran BM, Beg MMS (2018) Elements of sketching with words (an extended paper). Int J Granul Comput Rough Sets Intell Syst (Inderscience Publishers, UK). ISBN:978-1-4244-7964-1. pp 241–246. https://doi.org/10.1109/GrC.2010.47

  12. Imran BM, Beg MMS (2012) Fuzzy identification of geometric shapes. In: International conference on computer science and information technology 2012, Bangalore

  13. Imran BM, Beg MMS (2011) Towards computational forensics with f-geometry. In: World conference on soft computing, 2011; San Francisco State University, California, USA

  14. Imran BM, Beg MMS (2018) Fuzzy towards perception based image retrieval. In: International conference on computer science and information technology, Bangalore

  15. Zadeh LA (1996) Fuzzy sets and information granularity. In: Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems. pp 433–448. https://doi.org/10.1142/9789814261302_0022

  16. VishwakarmaVirendra P (2013) Illumination normalization using fuzzy filter in DCT domain for face recognition. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-013-0207-z

    Google Scholar 

  17. Wang X, He Y, Dong L, Zhao H (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(9):4230–4252

    Article  MATH  Google Scholar 

  18. Wang X, Wang Y, Xu X, Ling W, Yeung D (2001) A new approach to fuzzy rule generation: fuzzy extension matrix. Fuzzy Sets Syst 123(3):291–306

    Article  MathSciNet  MATH  Google Scholar 

  19. Cresswell MJ (1973) Logic and languages. Methuen, London

    Google Scholar 

  20. Huynh VN, Ho TB, Nakamori Y (2002) A parametric representation of linguistic hedges in Zadeh’s fuzzy logic. Int J Approx Reason 30:203–223

    Article  MathSciNet  MATH  Google Scholar 

  21. Narasimha Prasad LV, Prudhvi Kumar Reddy K, Naidu MM (2013) An efficient decision tree classifier to predict precipitation using gain ratio. Int J Soft Comput Softw Eng 3(3):674–682

    Google Scholar 

  22. Ibrahim N, Salam S, Emaliana K, Jalil NA, Norasikin MA, Nawawi MR (2013) License plate recognition (LPR): a review with experiments for Malaysia case study. Int J Soft Comput Softw Eng 3(3):83–93. https://doi.org/10.7321/jscse.v3.n3.15

    Google Scholar 

  23. Rahman A, Ahmad T, Beg MMS (2015) Ranking of fuzzy similar faces using relevance matrix and aggregation operators. In: International conference on soft computing and software engineering (SCSE 2015), March 5–6, University of California Berkeley, USA Procedia Computer Science, vol 62. pp 84–91

  24. Zadeh LA (1968) Probability measures of fuzzy events. J Math Anal Appl 23(2):421–427

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abdul Rahman.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahman, A., Beg, M.M.S. Face sketch recognition: an application of Z-numbers. Int. j. inf. tecnol. 11, 541–548 (2019). https://doi.org/10.1007/s41870-018-0178-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-018-0178-0

Keywords

Navigation