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Factex: A Practical Approach to Crime Detection

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Data Management, Analytics and Innovation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1042))

Abstract

The crime on roads is a major problem faced today by all the modern cities. Road Transport is the most common escape route for many criminals. Thefts and many other crimes remain unregistered and unsolved due to lack of evidence. Effective tracking of vehicles and criminals is still a big problem and involves plenty of resources. To evade such a condition, we have proposed a machine learning-based practical crime detection system using the text and face recognition techniques. Such systems will be proved useful in parking lots, toll stations, airports, border crossings, etc. In the proposed system, the text recognition involves extracting the characters present in the Indian number plates and the predicted output will be compared with the registered vehicle database. Simultaneously, Face recognition feature constitutes identifying criminal faces based on certain face regions and then mapping the respected coordinates with the criminal database. The proposed system presented in this research paper targets to deliver improvised outcomes considering the time constraints and accuracy with more than 85% successful recognitions in normal working conditions with the goal to accomplish the successful detection of crime using machine learning algorithms such as KNN, SVM, and face detection classifiers to present a practical real time detection.

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References

  1. Du, S., et al.: Automatic license plate recognition (Alpr): a state- of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311 (2013)

    Article  Google Scholar 

  2. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of the IEEE, pp. 586–591 (1991)

    Google Scholar 

  3. Bachhety, Shivam, Singhal, Ramneek, Rawat, Kshitiz, Joshi, Kunal, Jain, Rachna: Crime detection using text recognition and face recognition. Int. J. Pure Appl. Math. 119(15), 2797–2807 (2018)

    Google Scholar 

  4. Do, H.N., Vo, M.T., Vuong, B.Q., Pham, H.T., Nguyen, A.H.: Automatic license plate recognition using mobile device. In: 2016 International Conference on Advanced Technologies for Communications (ATC), pp. 268–271. IEEE (2016)

    Google Scholar 

  5. Gou, C., Wang, K., Yao, Y., Li, Z.: Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines. IEEE Trans. Intell. Transp. Syst. 17(4), 1096–1107 (2016)

    Article  Google Scholar 

  6. Radmanesh, A.: A real time vehicle’s license plate recognition system. In: Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS’03), vol 4, pp. 159–167 (2015)

    Google Scholar 

  7. Wei-gang, Z., et al.: A study of locating vehicle license plate based on color feature and mathematical morphology. In: 6th International Conference on Signal Processing, vol 1, pp. 748-751 (2002)

    Google Scholar 

  8. Osuna, E., Freund, R., Girosi., F.: Training support vector machines: an application to face detection. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, June, pp. 130–136, (1997)

    Google Scholar 

  9. Schneiderman, H., Kenade, T.: Probabilistic modeling of local appearance and spatial relationships for object recognition. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 45–51 (1998)

    Google Scholar 

  10. Saraswathi, M., et al.: Evaluation of PCA and LDA techniques for face recognition using ORL face database. Int. J. Comput. Sci. Inf. Technol. 6(1), 810–813 (2015)

    Google Scholar 

  11. GPU Based Face Recognition system for authentication, Bhumika Agrawal, et.al, © 2017 IJEDR|Volume 5, Issue 2|ISSN: 2321–9939

    Google Scholar 

  12. Yang, J., Luo, L., Qian, J., Tai, Y., Zhang, F., Xu, Y.: Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 156–171 (2017)

    Article  Google Scholar 

  13. Zhai, X., Bensaali, F., Sotudeh, R.: OCR-based neural network for ANPR. In: 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings 2012 Jul 16, IEEE, Jul 16, pp. 393–397 (2012)

    Google Scholar 

  14. Chellappa, R., Ni, J., Patel, V.M.: Remote identification of faces: problems, prospects, and progress. Pattern Recogn. Lett. 33, 1849–1859 (2012)

    Article  Google Scholar 

  15. Yetirajam, M., Nayak, M.R., Chattopadhyay, S.: Recognition and classification of broken characters using feed forward neural network to enhance an OCR solution. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 1 (2012)

    Google Scholar 

  16. Almudhahka, N.Y., Nixon, M.S., Hare, J.S.: Automatic semantic face recognition. 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), Washington, DC, 2017, pp. 180–185

    Google Scholar 

  17. Lund, W.B., Kennard, D.J., Ringger, E.K.: Combining multiple thresholding binarization values to improve OCR output. Presented in document recognition and retrieval XX Conference 2013, California, USA, 2013. USA: SPIE (2013)

    Google Scholar 

  18. Satti, D.A.: Offline Urdu Nastaliq OCR for printed text using analytical approach. MS thesis report Quaid-i-Azam University: Islamabad, Pakistan. p. 141 (2013)

    Google Scholar 

  19. Bhansali, M., Kumar, P.: An alternative method for facilitating cheque clearance using smart phones application. Int. J. Appl. Innov. Eng. Manag. (IJAIEM) 2(1), 211–217 (2013)

    Google Scholar 

  20. Roy, A., Ghoshal, D.P.: Number plate Recognition for use in different countries using an improved segmentation. In: 2nd National Conference on Emerging Trends and Applications

    Google Scholar 

  21. Zhang, K., Zhang, Z., Li, Z.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  22. Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Loumos, V., Kayafas, E.: A license plate-recognition algorithm for intelligent transportation system applications. IEEE Trans. Intell. Transp. Syst. 7(3), 377–392 (2006)

    Article  Google Scholar 

  23. Erdinc Kocer, H., Kursat Cevik, K.: Artificial neural networks-based vehicle license plate recognition. Proced. Comput. Sci. 3, 1033–1037 (2011)

    Article  Google Scholar 

  24. Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 1450–1466 (2002)

    Article  Google Scholar 

  25. Mita, T., Kaneko, T., Hori, O.: Joint Haar-like features for face detection. In: IEEE International Conference on Computer Vision, pp. 1550–5499 (2005)

    Google Scholar 

  26. Almudhahka, N.Y., Nixon, M.S., Hare, J.S.: Automatic semantic face recognition. In: 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), Washington, DC, pp. 180–185 (2017)

    Google Scholar 

  27. Chen, Z., Huang, W., Lv, Z.: Towards a face recognition method based on uncorrelated discriminant sparse preserving projection. Multimed. Tools Appl. 76, 17669 (2017). https://doi.org/10.1007/s11042-015-2882-0

    Article  Google Scholar 

  28. Dillak, R.Y., Dana, S., Beily, M.: Face recognition using 3D GLCM and Elman Levenberg recurrent neural network. In: International Seminar on Application for Technology of Information and Communication (ISemantic), pp. 152–156 (2016)

    Google Scholar 

  29. Winarno, E., Hadikurniawati, W., Rosso, R.N.: Location based service for presence system using haversine method. In: 2017 International Conference on Innovative and Creative Information Technology (ICITech), pp. 1–4 (2017)

    Google Scholar 

  30. Qi, X., Liu, C., Schuckers, S.: CNN based key frame extraction for face in video recognition. In: 2018 IEEE 4th International Conference on Identity Security and Behavior Analysis (ISBA), pp. 1–8 (2018)

    Google Scholar 

  31. Deshpande, N.T.: Face detection and recognition using viola- jones algorithm and fusion of PCA and ANN. Adv. Comput. Sci. Technol. 10(5), 1173–1189 (2017)

    Google Scholar 

  32. Zohra, F.T., Gavrilov, A.D., Duran, O.Z., Gavrilova, M.: A linear regression model for estimating facial image quality. In: 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), pp. 130–138 (2017)

    Google Scholar 

  33. Dhawanpatil, T., Joglekar, B.: Face spoofing detection using multiscale local binary pattern approach. In: 2017 International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5 (2017)

    Google Scholar 

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Acknowledgements

This research project is a sole work of the authors mention in this paper and no organization has participated in its contribution. All experimentation performed involving human participants were in accordance with the ethical standards and their consent. There is no violation of copyrights in use of the images of the vehicle number plate. The authors declare that they have no conflict of interest [32, 33].

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Authors and Affiliations

  1. Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Rachna Jain & Shivam Bachhety

  2. Graduate School, Duy Tan University, Da Nang, Vietnam

    Anand Nayyar

Authors
  1. Rachna Jain
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  2. Anand Nayyar
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  3. Shivam Bachhety
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Corresponding author

Correspondence to Anand Nayyar .

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Editors and Affiliations

  1. Society for Data Science, Pune, Maharashtra, India

    Neha Sharma

  2. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Amlan Chakrabarti

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Jain, R., Nayyar, A., Bachhety, S. (2020). Factex: A Practical Approach to Crime Detection. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-32-9949-8_35

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  • DOI: https://doi.org/10.1007/978-981-32-9949-8_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9948-1

  • Online ISBN: 978-981-32-9949-8

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