Smart Physical Intruder Detection System for Highly Sensitive Area

In this ever-growing world of automation and digitization, where data is a pivotal element for the growth of every individual, institution, and organization, whether digital or physical, data could also be the reason for destruction, if acquired by an antagonist through unconventional access. Data is a very sensitive point in all the domains ranging from an individual’s personal space to tactical military centers such as defense institutions, military matters, financial institutions, hospitals, and educational institutions. Thus, it is necessary to protect the data from intruders. Physical Intruder Detection is equally important as the detection of intrusion in computer networks. Though the later is always digital and without manual intervention. Physical Intruder Detection can be either digital or done manually. The paper presents a system for an enclosed area, based on IoT and supported by Digital Image Processing, to capture any Physical Intruder who breaches the security system and alert the rightful person regarding the intrusion. The approach uses the PIR motion sensor to detect any suspicious activity, turn on the webcam and with the help of Face Recognition System using Digital Image Processing, recognizes whether it is the rightful person or not. If it is an Intruder, then the webcam will start recording the activity of the Intruder and send a text message as well as an email to the system owner alerting him/her/them about the intrusion. A link to this live feed to the system owner is also attached to the alert message and mail. This Intruder Detection System is energy efficient as well because the webcam will be turned on only when the motion sensor detects any suspicious activity.

[1]  P. A. Ramamoorthy,et al.  Principal Component Analysis Based Feature Extraction, Morphological Edge Detection and Localization for Fast Iris Recognition , 2012 .

[2]  Ravi Kishore Kodali,et al.  MQTT based home automation system using ESP8266 , 2016, 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC).

[3]  Antonio Albiol,et al.  A Comparative Study of Facial Landmark Localization Methods for Face Recognition Using HOG descriptors , 2010, 2010 20th International Conference on Pattern Recognition.

[4]  B. J. Beggs,et al.  Automatic video surveillance with intelligent scene monitoring and intruder detection , 1996, 1996 30th Annual International Carnahan Conference on Security Technology.

[5]  Oludele Awodele,et al.  Design of an Automated Intrusion Detection System incorporating an Alarm , 2009, ArXiv.

[6]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[8]  Roland Geraerts,et al.  A comparative study of k‐nearest neighbour techniques in crowd simulation , 2017, Comput. Animat. Virtual Worlds.

[9]  S. Imandoust,et al.  Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background , 2013 .

[10]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[11]  Hernando Fernandez-Canque,et al.  Automatic intruder detection incorporating intelligent scene monitoring with video surveillance , 1997 .

[12]  Pablo Laguna,et al.  Principal Component Analysis in ECG Signal Processing , 2007, EURASIP J. Adv. Signal Process..

[13]  Eui-nam Huh,et al.  Low cost real-time system monitoring using Raspberry Pi , 2015, 2015 Seventh International Conference on Ubiquitous and Future Networks.

[14]  Q. Ruan,et al.  Efficient Kernel Discriminate Spectral Regression for 3D face recognition , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[15]  Antonio Albiol,et al.  Precise eye localization using HOG descriptors , 2011, Machine Vision and Applications.

[16]  Husni Teja Sukmana,et al.  Prototype utilization of PIR motion sensor for real time surveillance system and web-enabled lamp automation , 2015, 2015 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob).

[17]  Lin-Lin Huang,et al.  Face detection from cluttered images using a polynomial neural network , 2003, Neurocomputing.

[18]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[19]  Liton Chandra Paul,et al.  Methodological analysis of Principal Component Analysis (PCA) method , 2013 .