Attendance Management system using Face Recognition
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| Natura: | Recurso digital |
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Zenodo
2021
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| _version_ | 1866901495170990080 |
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| author | Sandhya Potadar Riya Fale Prajakta Kothawade Arati Padale |
| author_facet | Sandhya Potadar Riya Fale Prajakta Kothawade Arati Padale |
| contents | Managing attendance can be a tedious job when implemented by traditional methods like calling out roll calls or taking a student's signature. To solve this issue, a smart and authenticated attendance system needs to be implemented. Generally, biometrics such as face recognition, fingerprint, DNA, retina, iris recognition, hand geometry etc. are used to execute smart attendance systems. Face is a unique identification of humans due to their distinct facial features. Face recognition systems are useful in many real-life applications. In the proposed system, initially all the students will be enrolled by storing their facial images with a unique ID. At the time of attendance, real time images will be captured and the faces in those images will be matched with the faces in the pre-trained dataset. The Haar cascade algorithm is used for face detection. Local Binary Patterns Histogram (LBPH) algorithm is used for face recognition and training the stored dataset, that generates the histogram for stored images and the real time image. To recognize the face, the difference between histograms of real time image & dataset images is calculated. Lower difference gives the best match resulting in displaying the name & roll number of that student. Attendance of the student is automatically updated in the excel sheet. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18536101 |
| institution | Zenodo |
| language | |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Attendance Management system using Face Recognition Sandhya Potadar Riya Fale Prajakta Kothawade Arati Padale Face detection & recognition Haar Cascade Classifier Local binary pattern histogram (LBPH). Managing attendance can be a tedious job when implemented by traditional methods like calling out roll calls or taking a student's signature. To solve this issue, a smart and authenticated attendance system needs to be implemented. Generally, biometrics such as face recognition, fingerprint, DNA, retina, iris recognition, hand geometry etc. are used to execute smart attendance systems. Face is a unique identification of humans due to their distinct facial features. Face recognition systems are useful in many real-life applications. In the proposed system, initially all the students will be enrolled by storing their facial images with a unique ID. At the time of attendance, real time images will be captured and the faces in those images will be matched with the faces in the pre-trained dataset. The Haar cascade algorithm is used for face detection. Local Binary Patterns Histogram (LBPH) algorithm is used for face recognition and training the stored dataset, that generates the histogram for stored images and the real time image. To recognize the face, the difference between histograms of real time image & dataset images is calculated. Lower difference gives the best match resulting in displaying the name & roll number of that student. Attendance of the student is automatically updated in the excel sheet. |
| title | Attendance Management system using Face Recognition |
| topic | Face detection & recognition Haar Cascade Classifier Local binary pattern histogram (LBPH). |
| url | https://doi.org/10.5281/zenodo.18536101 |