Ensemble Learning Approach for Weapon Recognition Using Images of Wound Patterns: A Forensic Perspective

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Dayanand G Savakar 1,* Anil Kannur 2

1. Department of Computer Science, Rani Channamma University Belagavi India

2. Department of Computer Engineering, A.G. Patil Institute of Technology Solapur India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2018.11.01

Received: 19 Jul. 2018 / Revised: 16 Aug. 2018 / Accepted: 17 Sep. 2018 / Published: 8 Nov. 2018

Index Terms

Classifiers, Ensemble, Forensic, Recognition, Patterns, Weapons, Wound


This paper presents a forensic perspective way of recognizing the weapons by processing wound patterns using ensemble learning that gives an effective forensic computational approach for the distinguished weapons used in most of crime cases. This will be one of the computational and effective substitutes to investigate the weapons used in crime, the methodology uses the collective wound patterns images from the human body for the recognition. The ensemble learning used in this proposed methodology improves the accuracy of machine learning methods by combining several methods and predicting the final accuracy by meta-classifier. It has given better recognition process compared to single individual model and the traditional method. Ensemble learning is more flexible in function and is better in the wound pattern recognition and their respective weapons as it overcomes the issue to overfit training data. The result achieved for weapon recognition based on wound patterns is 98.34%, from existing database of 800 images of pattern consisting of wounds of stabbed and gunshots. The authenticated experiments out-turns the preeminence of projected method over the widespread feature extraction approach considered in the work and also compares and suggest the false positive recognition verses false negative recognition. The proposed methodology has given better results compared to traditional method and will be helpful in forensic and crime investigation.

Cite This Paper

Dayanand G Savakar, Anil Kannur, "Ensemble Learning Approach for Weapon Recognition Using Images of Wound Patterns: A Forensic Perspective", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.11, pp. 1-9, 2018. DOI: 10.5815/ijigsp.2018.11.01


[1]Shuaibur Rahman, M. N. A. Khan, (2016) "Digital Forensics through Application Behavior Analysis", International Journal of Modern Education and Computer Science (IJMECS), Vol.8, No.6, pp.50-56. 

[2]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. 

[3]Adeleh Farzad, Rahebeh Niaraki Asli, (2015), "Recognition and Classification of Human Behavior in Intelligent Surveillance Systems using Hidden Markov Model", IJIGSP, vol.7, no.12, pp.31-38. 

[4]Varsha Dixit and Anupam Agrawal, (2015), “Real Time Hand Detection & Tracking for Dynamic Gesture Recognition”, International Journal of Intelligent Systems and Applications, Vol. 08, pp: 38-44 Published Online July 2015 in MECS. 

[5]Dayanand G Savakar, Anil Kannur (2015) “A Genetic algorithm and Bayesian approach for recognition & 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. 

[6]Dayanand G. Savakar and Anand Ghuli, (2015), “Digital Watermarking as distributed noise by Discrete Wavelet Transformation, Fast Fourier Transformation and Fast Walsh-Hadamard Transform to study the sensitivity between Robustness and Fidelity”, International Journal of Computer Application, Issue 5, Vol.1, pp 102-107, ISSN: 2250-1797. 

[7]Dayanand G. Savakar and Anand Ghuli, (2014), “Digital Watermarking-A Combined Approach by DWT, ChirpZ and Fast Walsh-Hadamard Transform”, International Journal of Computer Technology and Applications (IJCTA), Vol. 5 No.6, pp 2006-2010, ISSN 2229-6093. 

[8]Song Bo, (2012) “Automated wound recognition system based on image segmentation and Artificial Neural Networks”, IEEE International Conference on Bioinformatics and Biomedicine, pp: 11-16. 

[9]Gitto L., Vullo A., Demari G.M., (2012) “Recognition 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. 

[10]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. 

[11]Suapang P., et.al., (2011),” Tool and Firearm Recognition System Based on Image Processing”, Proceedings of 11th International Conference on Control, Automation and Systems, pp: 178 – 182 

[12]Kaliszan M., Karnecki K., Akçan R., (2011) “Striated abrasions from a knife with non-serrated blade— recognition 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  

[13]Ajay Kumar N, ChenyeWu, (2011) “Automated human recognition using ear imaging”, Journal of Pattern Recognition, Elsevier Ltd., pp: 1-13. 

[14]Basavaraj S. Anami and Dayanand G. Savakar, (2011), “Suitability of Feature Extraction Methods in Recognition 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. 

[15]Francisco Veredas, et.al, (2010),” Binary Tissue Classification on Wound Images with Neural Networks and Bayesian Classifiers”, IEEE transactions on medical imaging, Vol: 29, Issue2, pp: 410-426. 

[16]Anil Jain and Jung-Eun Lee, (2009), “Scars, marks, and tattoos: a soft biometric for identifying suspects and victims”, Journal of SPIE, the international society for optics and photonics, pp: 01-02 

[17]B.S. Anami, D.G. Savakar, (2009), “Effect of Foreign Bodies on Recognition and Classification of Bulk Food Grains Image Samples”, Journal of Applied Computer Science and Mathematics, Vol.3(6), pp: 77- 83. 

[18]F.A. Andaló, A.V. Miranda, A.X. Falcão, (2009),” Shape feature extraction and description based on tensor scale”, Journal of Pattern Recognition, Elsevier Ltd, pp:1-11. 

[19]B. S. Anami, Dayanand G. Savakar, (2009), “Recognition and Classification of Food grains, Fruits and Flowers Using Machine Vision”, International Journal of Food Engineering, Vol.5, Issue 4, pp: 1-25. 

[20]M. Brandon Westover and Joseph A. O’Sullivan, (2008) “Achievable Rates for Pattern Recognition”, IEEE transactions on Information Theory, Vol: 54, Issue No. 1, pp: 299-320. 

[21]Li Dongguang, (2008) “Firearm Recognition System Based on Ballistics Image Processing”, Proceedings of CISP '08, Congress on Image & Signal Processing Vol: 3, pp:149-154 

[22]Jie Liu1, Jigui Sun, Shengsheng Wang, (2006) “Pattern Recognition: An overview”, IJCSNS International Journal of Computer Science and Network Security, Vol:6, Issue No.6, pp: 57-61 

[23]Qi Peter Li, and Biing-Hwang Juang, (2006) “Study of a Fast-Discriminative Training Algorithm for Pattern Recognition”, IEEE transactions on neural networks, Vol: 17, Issue No. 5, pp-1212-1221 

[24]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. 

[25]Candida Ferreira, (2001) “Gene Expression Programming: A New Adaptive Algorithm for Solving Problems”, Journal of Complex Systems, Vol. 13, issue 2, pp: 87-129. 

[26]I.R. Coyle, D. Filed and P. Wenderoth (2009),” Pattern Recognition and Forensic Recognition: The Presumption of Scientific Accuracy and Other Falsehoods”, Criminal Law Journal, Vol. 33, Issue 4, pp:214-226 

[27]Louis Wehenkel, et.al., (2006), “Ensembles of extremely randomized trees and some generic applications”, RTE-VT workshop, Paris, May 29-30