Forensic Approach of Human Identification Using Dual Cross Pattern of Hand Radiographs

  • Sagar V. JoshiEmail author
  • Rajendra D. Kanphade
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


The demand for personal identification systems has augmented in recent years, due to serious accidents and required for criminal investigation. Under natural calamity and human-made disasters sometimes it is impossible to use traditional biometric techniques based on fingerprints, iris, and face; in such cases, biometric radiographs like dental, hand and skull are the great alternatives for the victim’s identification. The key objective of this study is to present a unique technique to deal with missing and unidentified person identification based on hand radiographs using Dual Cross Pattern (DCP). The proposed system has two main stages: feature vector extraction, and classification. In this paper, an attempt has been made to find out the most suitable classifier among k-nearest neighbor (k-NN) and Classification Tree based on the accuracy of retrieval of 10 subjects with 100 right-hand radiographs. The result achieved from experiments on a small primary database of radiographs reveals that matching hand radiographs based on DCP can be significantly used for human identification.


Ante-mortem (AM) radiographs Biometrics Dual Cross Pattern Postmortem (PM) radiographs 


  1. 1.
    Manigandan, T., Sumathy, C., Elumalai, M., Sathasivasubramanian, S., Kannan, A.: Forensic radiology in dentistry. J. Pharm. Bioallied Sci. 7(Suppl. 1), S260–S264 (2015)Google Scholar
  2. 2.
    Chen, H., Jain, A.K.: Dental biometrics: alignment and matching of dental radiographs. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1319–1326 (2005)CrossRefGoogle Scholar
  3. 3.
    Forensic identification of 9/11 victims ends, New York, 23 February 2005Google Scholar
  4. 4.
    Ross, A.A., Nandakumar, K., Jain, A.K.: Handbook of Biometrics. Springer, Heidelberg (2011)Google Scholar
  5. 5.
    Nomir, O., Abdel-Mottaleb, M.: Hierarchical contour matching for dental X-ray radiographs. Pattern Recognit. 41(1), 130–138 (2008)CrossRefGoogle Scholar
  6. 6.
    Abaza, A., Ross, A., Ammar, H.: Retrieving dental radiographs for post-mortem identification. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 2537–2540 (2009)Google Scholar
  7. 7.
    Senn, D.R., Stimson, P.G.: Forensic Dentistry, 2nd edn. Taylor & Francis, Boca Raton (2010)CrossRefGoogle Scholar
  8. 8.
    Jayaprakash, P.T., Srinivasan, G.J., Amravaneswaran, M.G.: Cranio-facial morph- analysis: a new method for enhancing reliability while identifying skulls by photo superimposition. Forensic Sci. Int. 117(1–2), 121–143 (2001)CrossRefGoogle Scholar
  9. 9.
    Pushparani, C., Ravichandran, C.P., Sivakumari, K.: Radiography superimposition in personal identification - a case study involving surgical implants. J. Forensic Res. 3, 140 (2012)CrossRefGoogle Scholar
  10. 10.
    Al-Amad, S., McCullough, M., Graham, J., Clement, J., Hill, A.: Craniofacial identification by computer-mediated superimposition. J. Forensic Odonto-Stomatol. 24(2), 47–52 (2006)Google Scholar
  11. 11.
    Nomir, O., Abdel-Mottaleb, M.: A system for human identification from X-ray dental radiographs. Pattern Recognit. 38(8), 1295–1305 (2005)CrossRefGoogle Scholar
  12. 12.
    Pathak, A., Pal, S.K., King, R.A.: Syntactic recognition of skeletal maturity. Pattern Recognit. Lett. 2(3), 193–197 (1984)CrossRefGoogle Scholar
  13. 13.
    Pathak, A., Pal, S.K.: Fuzzy grammars in syntactic recognition of skeletal maturity from X-rays. IEEE Trans. Syst. Man Cybern. 16(5), 657–667 (1986)CrossRefGoogle Scholar
  14. 14.
    Pietka, E.: Computer-assisted bone age assessment based on features automatically extracted from a hand radiograph. Comput. Med. Imaging Graph. 19(3), 251–259 (1995)CrossRefGoogle Scholar
  15. 15.
    Pietka, E., McNitt-Gray, M.F., Kuo, M.L., Huang, H.K.: Computer-assisted phalangeal analysis in skeletal age assessment. IEEE Trans. Med. Imaging 10(4), 616–620 (1991)CrossRefGoogle Scholar
  16. 16.
    Pietka, E., Kaabi, L., Kuo, M.L., Huang, H.K.: Feature extraction in carpal-bone analysis. IEEE Trans. Med. Imaging 12(1), 44–49 (1993)CrossRefGoogle Scholar
  17. 17.
    Kauffman, J.A., Slump, C.H., Moens, H.B.: Matching hand radiographs. In: Overview of the workshops ProRISC-SAFE, 17–18 November, Veldhoven, The Netherlands, pp. 629-633(2005)Google Scholar
  18. 18.
    Lin, P., Zheng, C., Zhang, F., Yang, Y.: X-ray carpal-bone image boundary feature analysis using region statistical feature based level set method for skeletal age assessment application. Opt. Appl. 2, 283–294 (2005)Google Scholar
  19. 19.
    Martin-Fernandez, M.A., Martin-Fernandez, M., Alberola-Lopez, C.: Automatic bone age assessment: a registration approach. In: Proceedings on Medical Imaging, SPIE, vol. 5032, pp. 1765–76, San Diego, CA (2003)Google Scholar
  20. 20.
    Aja-Fernandez, S., de Luis-Garcıa, R., Martın-Fernandez, M.A., Alberola-Lopez, C.: A computational TW3 classifier for skeletal maturity assessment. A computing with words approach. J. Biomed. Inform. 37(2), 99–107 (2004)CrossRefGoogle Scholar
  21. 21.
    Davis, L.M., Theobald, B.J., Lines, J., Toms, A., Bagnall, A.: On the segmentation and classification of hand radiographs. Int. J. Neural Syst. 22, 1250020 (2012)CrossRefGoogle Scholar
  22. 22.
    Hue, T.T.M., Kim, J.Y., Fahriddin, M.: Hand bone radiograph image segmentation with ROI merging. Recent Researches in Mathematical Methods in Electrical Engineering and Computer Science, pp. 147–154 (2011)Google Scholar
  23. 23.
    Mahmoodi, S., Sharif, B.S., Chester, E.G., Owen, J.P., Lee, R.: Skeletal growth estimation using radiographic image processing and analysis. IEEE Trans. Inf Technol. Biomed. 4(4), 292–297 (2000)CrossRefGoogle Scholar
  24. 24.
    Hsieh, C.W., Jong, T.L., Tiu, C.M.: Bone age estimation based on phalanx information with fuzzy constrain of carpals. Med. Biol. Eng. Comput. 45(3), 283–295 (2007)CrossRefGoogle Scholar
  25. 25.
    Mansourvar, M., Raj, R.G., Ismail, M.A., Kareem, S.A., Shanmugam, S., Wahid, S., et al.: Automated web based system for bone age assessment using histogram technique. Malays. J. Comput. Sci. 25(3), 107–121 (2012)Google Scholar
  26. 26.
    Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit. 43(3), 706–719 (2010)CrossRefGoogle Scholar
  27. 27.
    Ahmadian, A., Mostafa, A.: An efficient texture classification algorithm using Gabor wavelet. In: 2003 Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 930–933 (2003)Google Scholar
  28. 28.
    Ding, C., Choi, J., Tao, D., Davis, L.S.: Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 518–531 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of E&TC EngineeringDr. D. Y. Patil Institute of TechnologyPuneIndia

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