Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

ELKarazle, Khaled; Raman, Valliappan; Then, Patrick

doi:10.3233/jid-210019

Cited by 2 publications

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“…Another method ( ELKarazle, 2022 ) proposes the use of generative adversarial networks to improve the quality of images before feeding them into a CNN for age group classification. It has been shown in this study that the use of GANs in combination with CNNs enhances performance for a number of age grouping problems.…”

Section: Introductionmentioning

confidence: 99%

A space and time efficient convolutional neural network for age group estimation from facial images

Alsaleh

Perkgoz

2023

PeerJ Computer Science

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Background Age estimation has a wide range of applications, including security and surveillance, human-computer interaction, and biometrics. Facial aging is a stochastic process affected by various factors, such as lifestyle, habits, genetics, and the environment. Extracting age-related facial features to predict ages or age groups is a challenging problem that has attracted the attention of researchers in recent years. Various methods have been developed to solve the problem, including classification, regression-based methods, and soft computing approaches. Among these, the most successful results have been obtained by using neural network based artificial intelligence (AI) techniques such as convolutional neural networks (CNN). In particular, deep learning approaches have achieved improved accuracies by automatically extracting features from images of the human face. However, more improvements are still needed to achieve faster and more accurate results. Methods To address the aforementioned issues, this article proposes a space and time-efficient CNN method to extract distinct facial features from face images and classify them according to age group. The performance loss associated with using a small number of parameters to extract high-level features is compensated for by including a sufficient number of convolution layers. Additionally, we design and test suitable CNN structures that can handle smaller image sizes to assess the impact of size reduction on performance. Results To validate the proposed CNN method, we conducted experiments on the UTKFace and Facial-age datasets. The results demonstrated that the model outperformed recent studies in terms of classification accuracy and achieved an overall weighted F1-score of 87.84% for age-group classification problem.

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Section: Introductionmentioning

confidence: 99%

A space and time efficient convolutional neural network for age group estimation from facial images

Alsaleh

Perkgoz

2023

PeerJ Computer Science

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Facial Age Estimation Using Machine Learning Techniques: An Overview

ELKarazle

Raman²,

Then

2022

BDCC

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Automatic age estimation from facial images is an exciting machine learning topic that has attracted researchers’ attention over the past several years. Numerous human–computer interaction applications, such as targeted marketing, content access control, or soft-biometrics systems, employ age estimation models to carry out secondary tasks such as user filtering or identification. Despite the vast array of applications that could benefit from automatic age estimation, building an automatic age estimation system comes with issues such as data disparity, the unique ageing pattern of each individual, and facial photo quality. This paper provides a survey on the standard methods of building automatic age estimation models, the benchmark datasets for building these models, and some of the latest proposed pieces of literature that introduce new age estimation methods. Finally, we present and discuss the standard evaluation metrics used to assess age estimation models. In addition to the survey, we discuss the identified gaps in the reviewed literature and present recommendations for future research.

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Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

Cited by 2 publications

References 0 publications

A space and time efficient convolutional neural network for age group estimation from facial images

A space and time efficient convolutional neural network for age group estimation from facial images

Facial Age Estimation Using Machine Learning Techniques: An Overview

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