Neural network with agnostic meta-learning model for face-aging recognition NN-MAML for face-aging recognition
Document Type
Article
Publication Date
1-1-2022
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.
Keywords
Face Aging, Face Recognition, Artificial Neural Network, Meta Learning, CALFW
Divisions
fsktm
Funders
None
Publication Title
Malaysian Journal of Computer Science
Volume
35
Issue
1
Publisher
Univ Malaya
Publisher Location
UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR, 50603, MALAYSIA