Open AccessNEURAL NETWORK WITH AGNOSTIC META-LEARNING MODEL FOR FACE-AGING RECOGNITION
Author(s) -
Rasha R. Atallah,
Amirrudin Kamsin,
Maizatul Akmar Ismail,
Ahmad Sami Al-Shamayleh
Publication year - 2022
Publication title -
malaysian journal of computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.197
H-Index - 18
ISSN - 0127-9084
DOI - 10.22452/mjcs.vol35no1.4
Subject(s) - computer science , face (sociological concept) , artificial intelligence , facial recognition system , artificial neural network , deep learning , machine learning , authentication (law) , pattern recognition (psychology) , social science , computer security , sociology
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 face-aging 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.