Age estimation in facial images through transfer learning

This paper was aimed to address the problem of image-based human age estimation. It has the following main contributions. First, we provide a comparison of three hand-crafted image features and five deep convolutional neural networks (DCNNs). Secondly, we show that the use of pre-trained DCNNs as fe...

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Bibliographic Details
Published inMachine vision and applications Vol. 30; no. 1; pp. 177 - 187
Main Authors Dornaika, F., Arganda-Carreras, I., Belver, C.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 13.02.2019
Springer Nature B.V
Subjects
Artificial neural networks
Chronology
Communications Engineering
Computer Science
Domains
Feature extraction
Image Processing and Computer Vision
Knowledge management
Networks
Pattern Recognition
Retraining
Short Paper
Training
Vision systems
Regression
Age estimation
Mean Absolute Error
Transfer learning
Cross-database evaluation
Deep CNNs
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Summary:This paper was aimed to address the problem of image-based human age estimation. It has the following main contributions. First, we provide a comparison of three hand-crafted image features and five deep convolutional neural networks (DCNNs). Secondly, we show that the use of pre-trained DCNNs as feature extractors can transfer the knowledge of DCNNs to new datasets and domains that were not necessarily addressed in the training phase. This is achieved by only retraining a shallow regressor over the deep features. Thirdly, we provide a cross-database evaluation involving biological and apparent ages. The paper shows that transfer learning allows the use of pre-trained DCNNs regardless of the type of ages (apparent or biological) that is adopted in DCNN training. The experiments are carried out on three public databases: MORPH, PAL, and Chalearn2016.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-018-0976-1