Facial recognition for partially occluded faces
| Autor: | Omer Abdulhaleem Naser, Sharifah Mumtazah Syed Ahmad, Khairulmizam Samsudin, Marsyita Hanaf, Siti Mariam Binti Shafie, Nor Zarina Zamri |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Control and Optimization
Computer Networks and Communications Facial recognition Multi-task cascaded Database Landmark FaceNe Hardware and Architecture convolutional neural networks Support vector classification Signal Processing Electrical and Electronic Engineering Occluded faces Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 30:1846 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v30.i3.pp1846-1855 |
| Popis: | Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50% accuracy for the training set and 95% for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|