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A rough set based reasoning approach for criminal identification

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Abstract

As a supplement to mugshot detection, a new approach is proposed to capture the eyewitness’s visual perception in the form of symbolic representation. It reveals physiological and facial characteristics of criminal which help in their identification. A rough set theory based technique is introduced to model those symbolic representations. This approach provides an intuitive insight to process criminal’s imprecise and imperfect knowledge. We used a benchmark mug-shot dataset consisting of 300 criminals faces from the Chinese University of Hong Kong (CUHK) to study the correctness of our proposed model. We took the help of 105 students of Indian Institute of Information Technology, Allahabad, who were treated as eyewitness to depict the visual perception about 300 criminal faces of CUHK. The experimental verification is composed of two modes which are analogous to viewed sketches and forensic sketches. Like viewed sketches we have generated test case-I, where perception is given while looking at the photo whereas test case-II is like the forensic sketches where the description is given by recalling the memory. We have achieved encouraging results on the viewed sketch database as well as forensic sketch database.

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References

  1. Jain AK, Klare B, Park U (2012) Face matching and retrieval in forensics applications. MultiMedia IEEE 19(1):20

    Article  Google Scholar 

  2. Jain AK, Klare B, Park U (2011) Face recognition: some challenges in forensics. Automatic face and gesture recognition and workshops (FG 2011), 2011 IEEE international conference on, pp 726–733

  3. Gibson L (2008) Forensic art essentials. Elsevier, Oxford

    Google Scholar 

  4. Taylor K (2001) Forensic art and illustration. CRC Press, Boca Raton

    Google Scholar 

  5. Koshimizu H, Tominaga M, Fujiwara T, Murakami K (1999) On KANSEI facial image processing for computerized facial caricaturing system PICASSO. Systems, man, and cybernetics, 1999. IEEE SMC ’99 conference proceedings. 1999 IEEE international conference on, vol 6, pp 294–299

  6. Chen H, Xu Y-Q, Shum H-Y, Zhu S-C, Zheng N-N (2001) Example-based facial sketch generation with non-parametric sampling. Computer vision, 2001. ICCV 2001. In: Proceedings of eighth IEEE international conference on, vol 2, pp 433–438

  7. Wang J, Bao H, Zhou W, Peng Q, Yingqing X (2002) Automatic image-based pencil sketch rendering. J Comput Sci Technol 17(3):347–355

    Article  Google Scholar 

  8. Uhl RG, Lobo NDV (1996) A framework for recognizing a facial image from a police sketch. In: Proceedings of IEEE international conference, computer vision and pattern recognition

  9. Wang X, Tang X (2009) Face photo–sketch synthesis and recognition. Pattern Anal Mach Intell IEEE Trans 31(11):1955–1967

    Article  MathSciNet  Google Scholar 

  10. Klare B, Li Z, Jain AK (2011) Matching forensic sketches to mug-shot photos. IEEE Trans Pattern Anal Mach Intell 33(3):639–646

    Article  Google Scholar 

  11. Xiao B, Gao X, Tao D, Li X (2009) A new approach for face recognition by sketches in photos. Signal Process 89(8):1576–1588

    Article  Google Scholar 

  12. Singh AK, Nandi GC (2016) Visual perception-based criminal identification: a query-based approach. J Exp Theor Artif Intell 29(1):175–196

    Article  Google Scholar 

  13. Gao X, Zhong J, Tao D, Li X (2008) Local face sketch synthesis learning. Neurocomputing 71(1012):1921–1930

    Article  Google Scholar 

  14. Tang X, Wang X (2003) Face sketch synthesis and recognition. In: Proceedings of IEEE internatiaonl conference, computer vision

  15. Tang X, Wang X (2004) Face sketch recognition. IEEE Trans Circuits Syst Video Technol 14(1):50–57

    Article  Google Scholar 

  16. Liu Q, Tang X, Jin H, Lu H, Ma S (2005) A nonlinear approach for face sketch synthesis and recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, San Diego, CA, USA, pp 1005–1010

  17. Xiao B, Gao X, Tao D, Yuan Y, Li J (2010) Photo-sketch synthesis and recognition based on subspace learning. Neurocomputing 73(46):840–852

    Article  Google Scholar 

  18. Gudivada VN, Raghavan VV, Seetharaman GS (1993) An approach to interactive retrieval in face image databases based on semantic attributes. In: Proceedings of third annual symposium document analysis information retrieval

  19. Klare BF, Klum S, Klontz JC, Taborsky E, Akgul T, Jain AK (2014) Suspect identification based on descriptive facial attributes. In: Biometrics (IJCB), 2014 IEEE international joint conference on IEEE, pp 1–8

  20. Siddiquie B, Feris RS, Davis LS (2011) Image ranking and retrieval based on multi-attribute queries. In: Computer vision and pattern recognition (CVPR), 2011 IEEE conference on IEEE, pp 801–808

  21. Mane DR, Kale AD, Bhai MB, Hallikerimath S (2010) Anthropometric and anthroposcopic analysis of different shapes of faces in group of Indian population: A pilot study. J Forensic Legal Med 17(8):421–425

    Article  Google Scholar 

  22. http://en.wikipedia.org/wiki/VonLuschanschromaticscale. Accessed 29 Sept 2014

  23. http://www.beautylish.com/a/vcacj/whats-your-eye-shape. Accessed 29 Sept 2014

  24. http://design.tutsplus.com/tutorials/human-anatomy-fundamentals-advanced-facial-features-cms-20683. Accessed 29 Sept 2014

  25. Gudivada VN, Raghavan VV, Seetharaman GS (1993) An approach to interactive retrieval in face image databases based on semantic attributes. In: Proceedings of third annual symposium of document analysis information retrieval

  26. Kuei CA (1998) Face reading. M. Evans and Company, New York

    Google Scholar 

  27. Pawlak Z (1982) Rough sets. Int J Parallel Program 11(5):341–356

    MATH  Google Scholar 

  28. Pawlak Z (2002) Rough sets and intelligent data analysis. Inf Sci 147(14):1–12

    Article  MathSciNet  Google Scholar 

  29. Leung Y, Wei-Zhi W, Zhang W-X (2006) Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Oper Res 168(1):164–180

    Article  MathSciNet  Google Scholar 

  30. Dntsch I, Gediga G (1998) Uncertainty measures of rough set prediction. Artif Intell 106(1):109–137

    Article  MathSciNet  Google Scholar 

  31. Kryszkiewicz M (1998) Rough set approach to incomplete information systems. Inf Sci 112(14):39–49

    Article  MathSciNet  Google Scholar 

  32. Komorowski J, Pawlak Z, Polkowski L, Skowron A (1998) Rough sets: a tutorial. In: Rough fuzzy hybridization: a new trend in decision-making, pp 3–98

  33. Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177(1):3–27

    Article  MathSciNet  Google Scholar 

  34. Pawlak Z (2004) Elementary rough set granules: toward a rough set processor. Rough-neural computing. Springer, Berlin

    Google Scholar 

  35. Swiniarski RW (2001) Rough sets methods in feature reduction and classification. Int J Appl Math Comput Sci 11(3):565–582

    MathSciNet  MATH  Google Scholar 

  36. Khoo L-P, Tor S-B, Zhai L-Y (1999) A rough-set based approach for classification and rule induction. Int J Adv Manuf Tech 15:438–444

    Article  Google Scholar 

  37. Qian Y, Liang J, Dang C (2008) Converse approximation and rule extraction from decision tables in rough set theory. Comput Math Appl 55(8):1754–1765

    Article  MathSciNet  Google Scholar 

  38. Pawlak Z (1997) Rough set approach to knowledge-based decision support. Eur J Oper Res 99(1):48–57

    Article  Google Scholar 

  39. Qian Y, Liang J, Li D, Zhang H, Dang Chuangyin (2008) Measures for evaluating the decision performance of a decision table in rough set theory. Inf Sci 178(1):181–202

    Article  Google Scholar 

  40. Koriat A, Goldsmith M, Pansky A (2000) Toward a psychology of memory accuracy. Annu Rev Psychol 51(1):481–537

    Article  Google Scholar 

  41. Eisen ML, Morgan DY, Mickes L (2002) Individual differences in eyewitness memory and suggestibility: examining relations between acquiescence, dissociation and resistance to misleading information. Personal Indiv Differ 33(4):553–571

    Article  Google Scholar 

  42. Sarwar F, Allwood CM, Innes-Ker Å (2014) Effects of different types of forensic information on eyewitness memory and confidence accuracy. Eur J Psychol Appl Legal Context 6(1):17–27

    Article  Google Scholar 

  43. Forgas JP, Laham SM, Vargas PT (2005) Mood effects on eyewitness memory: affective influences on susceptibility to misinformation. J Exp Soc Psychol 41(6):574–588

    Article  Google Scholar 

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Acknowledgements

Author would like to thank Dr V. Joshi former doctor at the Banaras Hindu University (Varanasi), India for explaining the memory processes and neuro anatomy of limbic system. Gratitude is given to all the students of Robotics and Artificial Intelligence Lab Indian Institute of Information Technology Allahabad, India and those enrolled for HUR class. Special thanks to Chinese University of Hong Kong for providing their mug-shot database to accomplish this study.

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Authors and Affiliations

  1. Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology Allahabad, Allahabad, UP, 211012, India

    Avinash Kumar Singh & G. C. Nandi

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  1. Avinash Kumar Singh
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  2. Neha Baranwal
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  3. G. C. Nandi
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Correspondence to Avinash Kumar Singh.

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Singh, A.K., Baranwal, N. & Nandi, G.C. A rough set based reasoning approach for criminal identification. Int. J. Mach. Learn. & Cyber. 10, 413–431 (2019). https://doi.org/10.1007/s13042-017-0699-z

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  • DOI: https://doi.org/10.1007/s13042-017-0699-z

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