Age estimation in facial images through transfer learning

F. Dornaika; I. Arganda-Carreras; C. Belver

doi:10.1007/s00138-018-0976-1

Age estimation in facial images through transfer learning

Machine Vision and Applications ◽

10.1007/s00138-018-0976-1 ◽

2018 ◽

Vol 30 (1) ◽

pp. 177-187 ◽

Cited By ~ 6

Author(s):

F. Dornaika ◽

I. Arganda-Carreras ◽

C. Belver

Keyword(s):

Transfer Learning ◽

Age Estimation ◽

Facial Images

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Age Estimation from Facial Images using Transfer Learning and K-fold Cross-Validation

10.1145/3480651.3480659 ◽

2021 ◽

Author(s):

S.M. Shihab Uddin ◽

Md. Samin Morshed ◽

Mahruf Islam Prottoy ◽

A.B.M. Ashikur Rahman

Keyword(s):

Transfer Learning ◽

Age Estimation ◽

Cross Validation ◽

Facial Images ◽

Fold Cross Validation

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Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

Journal of Integrated Design and Process Science ◽

10.3233/jid-210019 ◽

2021 ◽

pp. 1-17

Author(s):

Khaled ELKarazle ◽

Valliappan Raman ◽

Patrick Then

Keyword(s):

Age Estimation ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Original Dataset ◽

Age Group Classification ◽

Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

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