University Classroom Attendance System Using FaceNet and Support Vector Machine

Nowadays, face recognition system becomes popular in research area. Face recognition is also used in many application areas such as attendance management system, people tracking system, and access control system. For multi-face recognition, it has still many challenges for detection and recognition because it is not easy to detect multiple faces from one frame and it is also difficult to recognize the faces with poor resolution. Therefore, the main objective of this paper is to get a better accuracy for multi-face recognition by using the combination of FaceNet and Support Vector Machine (SVM). In this proposed system, FaceNet is used for feature extraction by embedding 128 dimensions per face and SVM is used to classify the given training data with the extracted feature of FaceNet. University Classroom Attendance System is applied by the proposed multi-face recognition. The Experimental result show that the proposed approach is good enough for multi-face recognition with an accuracy of 99.6%. It is better than VGG16 model on the same data-set.