Iot Based Smart Home Security System with Face Recognition and Weapon Detection Using Computer Vision
M. Nandhini1, M. Mohamed Rabik2, Kiran Kumar3, Ashish Brahma4

1M. Nandhini, Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
2M.Mohamed Rabik, Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
3Kiran Kumar, Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
4Ashish Brahma, Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.

Manuscript received on September 21, 2020. | Revised Manuscript received on November 05, 2020. | Manuscript published on November 10, 2021. | PP: 336-344 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A80561110120| DOI: 10.35940/ijitee.A8056.1110120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Today home automation systems are popular in households. The control of electric fixtures like fans and lights is possible with the help of Internet of Things (IOT). The problem arises due to intrusion of burglars. The security of such systems has been done using computer vision and IOT. Here we aim to enhance this system by use of image processing for object detection. The system uses cameras at the door for face recognition as access control. Also, vibration and door magnet sensors are installed at the entry points to detect when the burglar tries to barge inside. PIR sensors are employed to detect human presence. A vibration sensor is also used to give alert if any shock nearby is detected. The system allows entry only if authorized person like owner or person registered on the database arrives. The person may be identified through valid proof of identity. It sends a message to the owner in case it doesn’t recognize the person within 20 seconds and the owner can monitor the activities via live feed from the camera. All sensor signals are checked and status of the system is updated continuously. In case the burglar tries to break inside, siren is activated and alert messages are redirected to the owner and the police. 
Keywords: Home Automation, IOT, Security system, Face recognition, Weapon detection, Vision system, Deep Learning.