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Development of Staff Attendance Management System using Fingerprint Biometric Identification Technique
| Content Provider | Semantic Scholar |
|---|---|
| Author | Obansola, Oluwatoyin Yemi Makinde, Oladayo Ezekiel Adeshina, Adesoji Henry Adebayo, Oyeronke |
| Copyright Year | 2016 |
| Abstract | Attendance management is the act of managing attendance or presence in a work setting. Considering an academic institute, the conventional method of taking staff attendance is by signing on paper is very time consuming and insecure, hence inefficient. Maintaining these records for a long time adds to the difficulty of the task. In lieu of this, an efficient system is proposed to solve the problem of manual attendance. In this research work, staff attendance is taken electronically with the help of a fingerprint Biometric system, and all the records are saved for subsequent operations. Fingerprints are considered to be the best and fastest method for biometric identification because they are secure to use, unique for every person and do not change in one's lifetime. Staff attendance management system using fingerprint biometric identification technique employs an automated system to calculate staff attendance in lecture rooms in an institution and do further calculations of daily and monthly attendance summary in order to reduce human errors during calculations. In essence, this system can be employed in curbing the problems of tardiness or lateness of lecturers to lecture rooms, impersonation and missing lecture periods in any institution. The system will also improve the productivity of any institution if properly implemented. |
| Starting Page | 55 |
| Ending Page | 69 |
| Page Count | 15 |
| File Format | PDF HTM / HTML |
| DOI | 10.15580/GJSETR.2016.3.101916185 |
| Volume Number | 6 |
| Alternate Webpage(s) | http://gjournals.org/GJSETR/Publication/2016/November/PDF/101916185%20Obansola%20et%20al.pdf |
| Alternate Webpage(s) | https://doi.org/10.15580/GJSETR.2016.3.101916185 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |