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Facial Recognition Attendance Checker
| Content Provider | Semantic Scholar |
|---|---|
| Author | Prompinit, Thanet Cheawcharnthong, Salisa Mongkolnam, P. Chan, Jonathan H. |
| Copyright Year | 2018 |
| Abstract | This work presents a novel facial recognition framework for attendance checking, by using the student’s smartphone and Bluetooth-low-energy (BLE) beacons placed in the classroom. Facial recognition is used for authentication based on the Active Appearance Model (68-point facial landmarks) that is converted to a 128-dimensional vector space. One or more beacons are used to track the sitting position of each student. Then image processing is used to analyze the current facial sentiment of the student. Facial recognition models based on Naïve Bayes, Support Vector Machine, and Random Forest were compared. The best model was found to be Random Forest with an accuracy of about 98% on the test data. A web-based application has been deployed and tested on Android smartphones to connect with the API for the needed services. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://sites.ieee.org/thailand-cis/files/2018/05/JSCI5_Paper_6.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
