Atallah, Rasha Ragheb and Kamsin, Amirrudin and Ismail, Maizatul Akmar and Al-Shamayleh, Ahmad Sami (2022) Neural network with agnostic meta-learning model for face-aging recognition NN-MAML for face-aging recognition. Malaysian Journal of Computer Science, 35 (1). pp. 56-69. ISSN 0127-9084, DOI https://doi.org/10.22452/mjcs.vol35no1.4.
Full text not available from this repository.Abstract
Face recognition is one of the most approachable and accessible authentication methods. It is also accepted by users, as it is non-invasive. However, aging results in changes in the texture and shape of a face. Hence, age is one of the factors that decreases the accuracy of face recognition. Face aging, or age progression, is thus a significant challenge in face recognition methods. This paper presents the use of artificial neural network with model-agnostic meta-learning (ANN-MAML) for face-aging recognition. Model-agnostic meta-learning (MAML) is a meta-learning method used to train a model using parameters obtained from identical tasks with certain updates. This study aims to design and model a framework to recognize face aging based on artificial neural network. In addition, the faceaging recognition framework is evaluated against previous frameworks. Furthermore, the performance and the accuracy of ANN-MAML was evaluated using the CALFW (Cross-Age LFW) dataset. A comparison with other methods showed superior performance by ANN-MAML.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Face Aging; Face Recognition; Artificial Neural Network; Meta Learning; CALFW |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 01 Aug 2022 00:59 |
| Last Modified: | 01 Aug 2022 00:59 |
| URI: | http://eprints.um.edu.my/id/eprint/33521 |
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