Implementation of FaceNet and support vector machine in a real-time web-based timekeeping application

Ly Quang Vu, Phan Thanh Trieu, Hoang-Sy Nguyen


This paper presents in detail how to build up and implement a real-time web-based face recognition application. The system works so that images of people are recorded and compared with the references on the database. If they match, the information about their presence will be recorded. As for the system architecture, the multi-task cascaded neural network was deployed for face detection. Followingly, for the recognizing tasks, we conducted a study to compare the accuracy level of three different face recognizing methods on three different public datasets by means of both the literature review and our simulation. From the comparison, it can be drawn that the FaceNet algorithm in-used with the support vector machine (SVM) classifier performs the best among others and is the most suitable candidate for the practical deployment. Eventually, the proposed system can deliver a highly satisfactory result, proving its potentials not only for the research but also the commercial purposes.


Face detection; Face recognition; Real-time web-based; Multi-task cascaded neural network; Support vector machine

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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