Hidden Markov model for identification of different marks on human body in forensic perspective

Автор: Dayanand G. Savakar, Anil Kannur

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 3 vol.11, 2019 года.

Бесплатный доступ

This paper proposes a computational forensic methodology which identify and classify different marks on the human body using Hidden Markov model. The methodology gives an efficient and effective computerized approach for the characteristics of different marks such as birthmarks, burntmarks, tattoos and weapons’ wounds found on human body. This proposed method will be a computationally effective substitution for the traditional forensic method in identifying the body marks in crime investigation of homicidal cases. Hidden Markov Model (HMM) is statistical and logical tool suitable for this identification. The marks on human body describe different patterns with characteristics that are helpful in identification. The experimental results achieved for identification of different marks with an average accuracy of 94.6%, on the available database of 400 images that includes four categories: Birthmarks, Burntmarks, Tattoos and weapons’ wounds (100 images of each marks). The methodology gives the better combination of features (color, texture and shape), which are extracted for the identification of marks on human body for the purpose of computational forensic science.

Еще

Birthmark, Burntmarks, Hidden Markov, Identification, Segmentation, Tattoos, Weapons, Wounds

Короткий адрес: https://sciup.org/15016838

IDR: 15016838   |   DOI: 10.5815/ijmecs.2019.03.06

Список литературы Hidden Markov model for identification of different marks on human body in forensic perspective

  • Mohammadreza Soltaninejad et.al., "Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI," International Journal of Computer Assisted Radiology and Surgery Springer, vol. 12, no. 1, pp. 183-203, 2017.
  • J. Raj et.al., "Medical Image Segmentation and Classification Using MKFCM and Hybrid Classifiers," International Journal of Intelligent Engineering and System, vol. 10, no. 6, pp. 9-19, 2017.
  • M. Lal et.al., "Skin Cancer Lesion Classification Using Lbp Based Hybrid Classifier," International Journal of Advanced Research in Computer Science, vol. 8, no. 7, pp. 993-997, 2017.
  • Rubayyi Alghamdi, et.al., (2016) “Hidden Markov Models (HMM) and Security Applications”, International Journal of Advanced Computer Science and Applications, Vol. 7, Issue 2, pp:39-47.
  • Dayanand G Savakar, Anil Kannur (2015) “A Genetic algorithm and Bayesian approach for identification & classification of weapon based on the stab wound patterns caused by different sharp metal”, International Journal of Computer Engineering and Applications, Volume IX, Issue I, pp: 01-12.
  • Song Bo, (2012) “Automated wound identification system based on image segmentation and Artificial Neural Networks”, IEEE International Conference on Bioinformatics and Biomedicine, pp: 11-16.
  • Gitto L., Vullo A., Demari G.M., (2012) “Identification of the murder weapon by the analysis of a typical pattern of sharp force injury”, Italian Journal of Legal Medicine, Vol: 01, Issue No. 1, pp: 04-14.
  • Ying Bai; Dali Wang, (2011)"Evaluate and identify optimal weapon systems using fuzzy multiple criteria decision making", Proceedings of IEEE International Conference on Fuzzy Systems, pp: 1510-1515.
  • Suapang P., Rangsit, Pathumthani, Yimmun S., Chumnan N.,(2011), “Tool and Firearm Identification System Based on Image Processing”, Proceedings of 11th International Conference on Control, Automation and Systems (ICCAS), pp: 178 – 182
  • Kaliszan M., Karnecki K., Akçan R., (2011) “Striated abrasions from a knife with non-serrated blade—identification of the instrument of crime on the basis of an experiment with material evidence”, International Journal of Legal Medicine, Vol: 125, Issue No. 5, pp: 745–748
  • Ajay Kumar N, ChenyeWu, (2011) “Automated human identification using ear imaging”, Journal of Pattern Identification, Elsevier Ltd., pp: 1-13.
  • Basavaraj S. Anami and Dayanand G. Savakar, (2011), “Suitability of Feature Extraction Methods in Identification and Classification of Grains, Fruits and Flowers”, International Journal of Food Engineering, Vol.7, Issue 1, Article 9, pp: 1-28, Publisher: Berkeley Electronic Press, Berkeley, U.S.A.
  • Francisco Veredas, Héctor Mesa, and Laura Morente, (2010), “Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers”, IEEE transactions on medical imaging, Vol: 29, Issue No. 2, pp: 410-426.
  • B.S.Anami, D.G.Savakar, (2009), “Effect of Foreign Bodies on Identification and Classification of Bulk Food Grains Image Samples”, Journal of Applied Computer Science and Mathematics, Vol.3(6), pp: 77- 83.
  • F.A. Andaló, A.V. Miranda, A.X.Falcão, (2009), “Shape feature extraction and description based on tensor scale”, Journal of Pattern Identification, Elsevier Ltd, pp:1-11.
  • B. S. Anami, Dayanand G. Savakar, (2009), “Identification and Classification of Food grains, Fruits and Flowers Using Machine Vision”, International Journal of Food Engineering, Vol.5, Issue 4, pp: 1-25.
  • M. Brandon Westover and Joseph A. O’Sullivan, (2008) “Achievable Rates for Pattern Identification”, IEEE transactions on Information Theory, Vol: 54, Issue No. 1, pp: 299-320.
  • Li Dongguang, (2008) “Firearm Identification System Based on Ballistics Image Processing”, Proceedings of CISP '08, Congress on Image and Signal Processing Vol: 3, pp: 149 – 154
  • Jie Liu1, Jigui Sun, Shengsheng Wang, (2006) “Pattern Identification: An overview”, IJCSNS International Journal of Computer Science and Network Security, Vol:6, Issue No.6, pp: 57-61
  • Qi Peter Li, and Biing-Hwang Juang, (2006) “Study of a Fast Discriminative Training Algorithm for Pattern Identification”, IEEE transactions on neural networks, Vol: 17, Issue No. 5, pp-1212-1221
  • T. Plattner, B. Kneubuehl, M. Thali, U. Zollinger, (2003) “Gunshot residue patterns on skin in angled-contact and near contact gunshot wounds”, Forensic Science International, Elsevier publication, Vol. 138, pp:68-74.
  • D. Hickman et.al., “Forensic image comparison techniques”, The IEE International Symposium on Imaging for Crime Detection and Prevention, 2005. ICDP 2005.
  • C. Smith et.al.,, "Identification of Traumatic Injury in Burned Cranial Bone: An Experimental Approach," Journal of Forensic Sciences, vol. 49, no. 3, pp. 1-10, 2004.
Еще
Статья научная