Publication: Asymmetric cascade face detector on multi-camera images
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Sirajdin Olagoke Adeshina | |
| dc.date.accessioned | 2025-05-08T03:26:54Z | |
| dc.date.available | 2025-05-08T03:26:54Z | |
| dc.date.issued | 2023-09-01 | |
| dc.description.abstract | Classroom attendance is one of the measures that ensure students’ presence and punctuality in day-to-day classroom activities. Researchers emphasize its impact on the academic performance of students. The traditional method of carrying out this measure is of no standard in today’s classroom attendance system because of population, time, sophistication, exhaustiveness, classroom size, and manipulative influence. The conventional automated classroom attendance systems are designed for small classrooms using a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture theatre attendance system is considered. the USM DK5 lecture theatre containing 14 volunteered participants (13 males and a female) of different races was used for the exercise. A move-shift seating arrangement, producing a total of 62 multiple-face datasets was obtained which was used for the evaluation of the proposed algorithm. An asymmetric cascade face detector was trained using faces and non-face samples. Selected Haar-like features were used based on their attention focus, area and width-to-height ratio, and feature specialization to train the algorithm. The asymmetric goal is set to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR). A relationship between the two factors (FRR and FAR) was established using a constant (l) as a trade-off between the two factors for automatic adjustment during training. Consequently, a TPR comparison of the proposed approach with the state-of-the-art tiny face (ResNet101) deep learning algorithm on images captured in USM (DK5) lecture theatre produced 96.3% to 99.6%, with an overhead constraint of 22.6s to 650.3s respectively. Tiny face as a deep learning approach with high level of accuracy, its limitations lie on the training data required, computational complexities, and implementation on the resource constraint platforms. Based on the experimental results on classroom datasets, the proposed approach shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.02s execution time per image as compared to 2.38s of the original algorithm. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/21557 | |
| dc.language.iso | en | |
| dc.title | Asymmetric cascade face detector on multi-camera images | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |