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Article

Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Frontal Face Images

School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
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Symmetry 2018, 10(9), 385; https://doi.org/10.3390/sym10090385
Received: 26 July 2018 / Revised: 26 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Special Issue Deep Learning for Facial Informatics)
Recently, there have been many studies on the automatic extraction of facial information using machine learning. Age estimation from frontal face images is becoming important, with various applications. Our proposed work is based on a binary classifier that only determines whether two input images are clustered in a similar class and trains a convolutional neural network (CNN) model using the deep metric learning method based on the Siamese network. To converge the results of the training Siamese network, two classes, for which age differences are below a certain level of distance, are considered as the same class, so the ratio of positive database images is increased. The deep metric learning method trains the CNN model to measure similarity based only on age data, but we found that the accumulated gender data can also be used to compare ages. Thus, we adopted a multi-task learning approach to consider the gender data for more accurate age estimation. In the experiment, we evaluated our approach using MORPH and MegaAge-Asian datasets, and compared gender classification accuracy only using age data from the training images. In addition, using gender classification, our proposed architecture, which is trained with only age data, performs age comparison using the self-generated gender feature. The accuracy enhancement by multi-task learning, i.e. simultaneously considering age and gender data, is discussed. Our approach results in the best accuracy among the methods based on deep metric learning on MORPH dataset. Additionally, our method has better results than the state of the art in terms of age estimation on MegaAge-Asian and MORPH datasets. View Full-Text
Keywords: convolutional neural network (CNN); deep metric learning; multi-task learning; image classification; age estimation convolutional neural network (CNN); deep metric learning; multi-task learning; image classification; age estimation
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MDPI and ACS Style

Jeong, Y.; Lee, S.; Park, D.; Park, K.H. Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Frontal Face Images. Symmetry 2018, 10, 385. https://doi.org/10.3390/sym10090385

AMA Style

Jeong Y, Lee S, Park D, Park KH. Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Frontal Face Images. Symmetry. 2018; 10(9):385. https://doi.org/10.3390/sym10090385

Chicago/Turabian Style

Jeong, Yoosoo, Seungmin Lee, Daejin Park, and Kil H. Park 2018. "Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Frontal Face Images" Symmetry 10, no. 9: 385. https://doi.org/10.3390/sym10090385

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