Student Attendance System Using Face Detection Features Based on Cognitive Internet of Things
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Abstract
Attendance fraud is often found in the presence of fake attendance from students. To overcome this problem, we apply the Haar-Like Feature Cascade method as the basis for building a face attendance system based on the Cognitive Internet of Things (CIoT). The proposed system works by representing local intensity patterns on the face image so that it will recognize students even from various frontal positions. The results of this research show a precise student face recognition that is able to recognize faces well. The conclusion of this research is that the Haar-like Feature Cascade method is able to detect faces precisely and can be used as a basis for developing student attendance technology based on the Cognitive Internet of Things.
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