Abstract
This research work presents a vehicle security system for safeguarding the vehicle from theft issues under the architectural design of capturing and comparing the vehicle user’s face. Since the face of human beings are unique and has different biometric characteristics which are complex to make fraudulent activities. Authenticating the vehicle users with face recognition mechanism is highly secured than token-based and knowledge-based security mechanisms. This research ultimately models and classifies the vehicle users into authorized and unauthorized users. Initially, an experimental prototype for vehicle security system is developed, and the application of image processing algorithms is incorporated into the model. The system uses Haar feature-based cascade classifier and AdaBoost method which is a machine learning algorithm used for detecting the authorized user’s face effectively. The algorithm is trained initially with appropriate amount of positive and negative images, and the feature gets extracted. When the person tries to access the vehicle, the experimental system captures the image of the person and makes comparison with extracted features to identify the authorized user. Finally, the results obtained from the prototype system are satisfied and beneficial against the issue of vehicle theft.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nored, L.S., Carlan, P., Downey, R.A.: A Brief Introduction to Criminal Law. Jones & Bartlett Publishers (2015)
Suganya, R., Kashwan, K.R.: FPGA implementation of anti-theft intelligent traffic management system. In: Reddy, M.S., Viswanath, K., Shiva Prasad, K.M. (eds.) International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications. Advances in Intelligent Systems and Computing, vol. 628. Springer, Singapore (2018)
Song, H., Zhu, S., Cao, G.: Svats: a sensor-network-based vehicle anti-theft system. Networking and Security Research Center, Department of Computer Science and Engineering, Pennsylvania State University, Technical Report NAS-TR-0076-2007, August 2007
Agustine, L., Pangaliela, E., Pranjoto, H.: Vehicle security and management system on GPS assisted vehicle using geofence and Google map. In: Pasila, F., Tanoto, Y., Lim, R., Santoso, M., Pah, N. (eds.) Proceedings of Second International Conference on Electrical Systems, Technology and Information 2015 (ICESTI 2015). Lecture Notes in Electrical Engineering, vol. 365. Springer, Singapore (2016)
Jia, L., Wu, D., Mei, L., Zhao, R., Wang, W., Yu, C.: Real-time vehicle detection and tracking system in street scenarios. In: Zhao, M., Sha, J. (eds.) Communications and Information Processing. Communications in Computer and Information Science, vol. 289. Springer, Berlin, Heidelberg (2012)
Zhong, W., Chen, G., Qi, H., Qi, X.H., Cheng, Z.: The design of vehicle anti-theft system based on the improved D-S evidence theory. In: 2014 8th International Conference on Future Generation Communication and Networking, Haikou, pp. 84–87 (2014)
Meng, X., Ou, Y., Lee, K.K., Xu, Y.: An intelligent vehicle security system based on modeling human driving behaviors. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.L., Yin, H. (eds.) Advances in Neural Networks—ISNN 2006. Lecture Notes in Computer Science, vol. 3973. Springer, Berlin, Heidelberg (2006)
Schmidt, A., Kasiński, A.: The performance of the haar cascade classifiers applied to the face and eyes detection. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds.) Computer Recognition Systems 2. Advances in Soft Computing, vol. 45. Springer, Berlin, Heidelberg (2007)
Sharma, P., Gupta, M.K., Mondal, A.K., Kaundal, V.: HAAR like feature-based car key detection using cascade classifier. In: Singh, R., Choudhury, S. (eds.) Proceeding of International Conference on Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol. 479. Springer, Singapore (2017)
Kasinski, A., Schmidt, A.: The architecture of the face and eyes detection system based on cascade classifiers. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds.) Computer Recognition Systems 2. Advances in Soft Computing, vol. 45. Springer, Berlin, Heidelberg (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on Computer Vision and Pattern Recognition (2001)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Finger-Print Recognition. Springer Science & Business Media, Berlin (2009)
Pavani, S.-K., Delgado, D., Frangi, A.F.: Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recogn. 43(1), 160–172 (2010)
Wilson, P.I., Fernandez, J.: Facial feature detection using Haar classifiers. ACM J. Comput. Sci. Coll. 21(4), 127–133 (2006)
Viola, P., Jones, M.J.: Robust real-time face detection. ACM Int. J. Comput. Vis. 57(2), 137–154 (2004)
Tian, H., Duan, Z., Abraham, A., Liu, H.: A novel multiplex cascade classifier for pedestrian detection. ACM Pattern Recogn. Lett. 34(14), 1687–1693 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pankajavalli, P.B., Vignesh, V., Karthick, G.S. (2019). Implementation of Haar Cascade Classifier for Vehicle Security System Based on Face Authentication Using Wireless Networks. In: Smys, S., Bestak, R., Chen, JZ., Kotuliak, I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-8681-6_58
Download citation
DOI: https://doi.org/10.1007/978-981-10-8681-6_58
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8680-9
Online ISBN: 978-981-10-8681-6
eBook Packages: EngineeringEngineering (R0)