Indonesian Journal of Electrical Engineering and Computer Science
Vol 21, No 3: March 2021

Real time face recognition of video surveillance system using haar cascade classifier

Adlan Hakim Ahmad (Independent Researcher, Shah Alam, Selangor, Malaysia)
Sharifah Saon (Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja, Batu Pahat, Johor, MALAYSIA)
Abd Kadir Mahamad (Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja, Batu Pahat, Johor, MALAYSIA)
Cahyo Darujati (1. Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia 2. Narotama University, Surabaya, Jawa Timur 60117, Indonesia.)
Sri Wiwoho Mudjanarko (Narotama University, Surabaya, Jawa Timur 60117, Indonesia.)
Supeno Mardi Susiki Nugroho (Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia)
Mochamad Hariadi (Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia)



Article Info

Publish Date
01 Mar 2021

Abstract

This project investigates the use of face recognition for a surveillance system. The normal video surveillance system uses in closed-circuit television (CCTV) to record video for security purpose. It is used to identify the identity of a person through their appearances on the recorded video, manually. Today’s video surveillance camera system usually not occupied with a face recognition system. With some modification, a surveillance camera system can be used as face detection and recognition that can be done in real-time. The proposed system makes use of surveillance camera system that can identify the identity of a person automatically by using face recognition of Haar cascade classifier. The hardware used for this project were Raspberry Pi as a processor and Pi Camera as a camera module. The development of this project consist of three main phases which were data gathering, training recognizer, and face recognition process. All three phases have been executed using Python programming and OpenCV library, which have been performed in a Raspbian operation system. From the result, the proposed system successfully displays the output result of human face recognition, with facial angle within ±40°, in medium and normal light condition, and within a distance of 0.4 to 1.2 meter. Targeted image are allowed to wear face accessory as long as not covering the face structure. In conclusion, this system considered, can reduce the cost of manpower in order to identify the identity of a person in real time situation.

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