International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Terrorist Distinguishment with Military Personnel on Attack in Air base Camps â€“ 3 Chamber Approach: Using Real Time Monitoring â€“ Arduino Sensor Detectors, Iris Recognition System & Wireless Communicating Nodes Deployed

Author : Shubham verma ¹ Dipti Ranjan ²

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Date of Publication :6th August 2022

Abstract: The security in the air base camps containing the nuclear assets and aircrafts has become a concern after interagency attacks from neighbouring countries. Manned Approach has proven to be of less efficiency in previous attacks. The research proposed 3C Chamber approach which is to be embedded into unmanned drones that will fly in the sky and recognise the terrorist intergencies. 1st chamber constitutes the 3 sensors connected with Arduino named EMAX 5300 which detects the explosives , DHT11 which is used to catch the humidity and body temperature of the running terrorists in the base camps and OV7670 is the Image sensor which will give the clear image and send to the second chamber which deals iris recognition with the image data so obtained and third chamber is basically the communication chamber that contains the database and deals with regional clusters in wireless communications , localisation of suspect area through network topology so mentioned in the later paper in form of clusters , alarm dissemination phase and to be time efficient we have time synchronisation in the third chamber. All the 3 chambers work simultaneously being connected to each other and respond to the centralised node with deals with action & response. In the second chamber experiment is carried out with UBRIS.V1 database and OpenCv with Training : Testing ratios as 60:40, 50-50, 40-60 â€“ the accuracy came out to be approximately 96.54%. Pre-registration phase involves the registration of the military personnel and later this data is retrieved and matched to catch and differentiate terrorists which is the main issue and concern of the research. The research is a combination of Biometrics , electronic sensors â€“ internet of things , wireless network communicatio

Reference :

1. Z. Huang, Y. Liu, C. Li, M. Yang, Chen A robust face and ear based multimodal biometric system using sparse representation, Pattern Recognition, 46 (2013) 2156–2168.
2. N. Alay, H.H. Al-Baity, A multimodal biometric system for personal verification based on different level fusion of iris and face traits. Biosci. Biotechnol. Res. Commun, 2 (2019), 565-576.
3. K. Gunasekaran, J. Raja, R. Pitchai, Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images. Automatika: Äasopis za automatiku, mjerenje, elektroniku, raÄunarstvo i komunikacije, 60(2019) 253-265.
4. Z.T. Liu, C-S Jiang, S-H Li, M. Wu, W-H Cao, M. Hao, Eye state detection based on Weight Binarization Convolution Neural Network and Transfer Learning, Applied Soft Computing,Volume, 109(2021),107565,ISSN 1568-4946,
5. H. Kim, J. Jo, K.A. Toh, J. Kim, Eye detection in a facial image under pose variation based on multi-scale iris shape feature, Image and Vision Computing, 57(2017) 147-164.
6. P. Majaranta, 2018. Bulling an Eye tracking and eye-based human–computer interaction, In Advances in Physiological Computing. Springer, London UK, 39–65.
7. L. Åšwirski, A. Bulling, N. Dodgson, 2012. Robust real-time pupil tracking in highly off-axis images. In Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara CA, USA. 173–176.
8. A. PÄƒsÄƒricÄƒ, R.G. Bozomitu, D. TÄƒrniceriu, G. Andruseac, H. Costin, Rotariu CAnalysis of Eye Image Segmentation Used in Eye Tracking Applications. Rev. Roum. Sci. Tech. 62(2017) 215–222.
9. N. Hezil, A. Boukrouche, Multimodal biometric recognition using human ear and palm print. IET Biom, 6(2017) 351–359.
10. A.A. Jarjes, K. Wang, G.J. Mohammed, Improved greedy snake model for detecting accurate pupil contour. IEEE 3rd international conference on advanced computer control, 2011, 515-519.
11. J.D. Bustard, M.S. Nixon, Robust 2D ear registration and recognition based on SIFT point matching, In2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems, 2008, 1-6.
12. J. Zhou, S. Cadavid, M. Abdel-Mottaleb,Exploiting Color SIFT Features for 2D ear recognition, 18th IEEE International Conference on image processing, 2011, 553 – 556.
13. R.A. Priyadharshini, S. Arivazhagan, M. Arun, Deep learning approach for person identification using ear biometrics, Appl Intell 51, 2161–2172 (2021). https://doi.org/10.1007/s10489 -020-01995-8
14. F. Khursheed, A.H. Mir, AR model based human identification using ear biometrics, International Journal of Signal Processing. Image Processing and Pattern Recognition, 7(2014) 347-360.
15. L. Alvarez, E. González, L. Mazorra, Fitting ear contour using an ovoid model, In Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology, 2005, 145-148. doi: 10.1109/CCST.2005.1594829.
16. B. Zavar, Arbab, S. Mark, On guided model-based analysis for ear biometrics, Comuter Vision and Image Understanding, 115(2011) 487-502.
17. J.G Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE transactions on pattern analysis and machine intelligence, 15(1993) 1148-1161. doi: 10.1109/34.244676
18. M. Rahul, R. Shukla, P.K. Goyal, Z.A. Siddiqui, V. Yadav, Gabor Filter and ICA-Based Facial Expression Recognition Using Two-Layered Hidden Markov Model, In Advances in Computational Intelligence and Communication Technology, 2021, 511-518. Springer, Singapore.
19. R.P. Wildes, Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(1997) 1348-1363. doi: 10.1109/5.628669