A practical aspect of identification and classifying of Guns based on gunshot wound patterns using gene expression programming

This paper describes a practical aspect of identification and classifying of Guns based on gunshot wound patterns. We mark a genuinely digitized approach for the characteristic and set of guns used in homicidal cases using Gene expression programming. This approach develops a computationally attractive and effective alternative to investigate the guns used in crime which uses the images of gunshot wound patterns available on the human body. The experimental results achieved for identification and classification accuracy of 91.1 and 93.4%, respectively, on the available database of 30 images including three categories: Hard-contact, Loose-contact and Angled-contact of each pattern consisting of gunshot wounds. Our experimental results from the authentication experiments and false positive identification verses false negative identification also suggest the superiority of the proposed approach over the other popular feature extraction approach considered in this work.

[1]  Basavaraj S. Anami,et al.  Suitability of Feature Extraction Methods in Recognition and Classification of Grains, Fruits and Flowers , 2011 .

[2]  L. Gitto,et al.  Identification of the murder weapon by the analysis of an atypical pattern of sharp force injury , 2012 .

[3]  Joseph A. O'Sullivan,et al.  Achievable Rates for Pattern Recognition , 2005, IEEE Transactions on Information Theory.

[4]  D. Savakar,et al.  Recognition and Classification of Food Grains, Fruits and Flowers Using Machine Vision , 2009 .

[5]  Ying Bai,et al.  Evaluate and identify optimal weapon systems using fuzzy multiple criteria decision making , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[6]  Héctor Mesa,et al.  Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers , 2010, IEEE Transactions on Medical Imaging.

[7]  Dongguang Li.  Firearm Identification System Based on Ballistics Image Processing , 2008, 2008 Congress on Image and Signal Processing.

[8]  Ricardo da Silva Torres,et al.  Shape feature extraction and description based on tensor scale , 2010, Pattern Recognit..

[9]  Zhang Jie,et al.  A New Adaptive Penalty Function Based on AEA Algorithm for Solving Constrained Optimization Problems and its Application in Process Optimization of Butene Alkylation , 2013 .

[10]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[11]  A. Ghuli,et al.  Digital Watermarking-A Combined Approach by DWT , Chirp-Z and Fast Walsh-Hadamard Transform , 2014 .

[12]  Chenye Wu,et al.  Automated human identification using ear imaging , 2012, Pattern Recognit..

[13]  Bo Song,et al.  Automated wound identification system based on image segmentation and Artificial Neural Networks , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

[14]  Piyamas Suapang,et al.  Tool and Firearm Identification System Based on Image Processing , 2011, 2011 11th International Conference on Control, Automation and Systems.

[15]  Biing-Hwang Juang,et al.  Study of a Fast Discriminative Training Algorithm for Pattern Recognition , 2006, IEEE Transactions on Neural Networks.

[16]  Z. Jankowski,et al.  Striated abrasions from a knife with non-serrated blade—identification of the instrument of crime on the basis of an experiment with material evidence , 2011, International Journal of Legal Medicine.

[17]  M. Thali,et al.  Gunshot residue patterns on skin in angled contact and near contact gunshot wounds. , 2003, Forensic science international.