Abstract—Closed-Circuit Television (CCTV) cameras are placed everywhere in both public and private areas and used in a broad range of applications, especially for security purposes. CCTVs are managed by CCTV operators for twenty-four hours to ensure that there are no excessive activities in the area. FEDSecurity is a monitoring system that alarm and capture the images of CCTV operator whenever falls slumbering during the time of work. Face and eye detection used Haar-Cascade Algorithm, and Microsoft SQL Server Express used as storage. FEDSecurity is also capable of determining whether the user in front of the camera is a real human or a picture by gauging the time that a user is not blinking eyes. Worst case scenarios could prevent possible suspicious activities when using the system. The system acquired was an asset to the homeowners, companies and any other business firms. Agile Software Development Method adapted in developing the system. In testing the system’s acceptability, the questionnaires were based on the ISO 9126 Standard. The respondents of the study are the IT professionals, CCTV operators such as security guard or security officers, and the management such as administrator or security head. The result of evaluation interpreted as very acceptable based on Likert's scale.
Index Terms—computer vision, CCTV, face and eye detection, monitoring system.
Roxanne A. Ancheta, Felizardo C. Reyes Jr., and Jasmin A. Caliwag are with the College of Information Technology Education, Information Technology Department of the Technological Institute of the Philippines Quezon City, Philippines (e-mail: email@example.com, firstname.lastname@example.org, email@example.com).
Reynaldo E. Castillo is with the College of Information Technology Education, Computer Science Department of the Technological Institute of the Philippines Quezon City, Philippines (e-mail: firstname.lastname@example.org).
Cite: Roxanne A. Ancheta, Felizardo C. Reyes Jr., Jasmin A. Caliwag, and Reynaldo E. Castillo, "FEDSecurity: Implementation of Computer Vision Thru Face and Eye Detection," International Journal of Machine Learning and Computing vol. 8, no. 6, pp. 619-624, 2018.