Abstract
This paper proposes an age estimation method from the age period using Triplet Network. Age estimation is still an active research topic in machine learning, and it can be formulated as a regression problem. Usually, a specific age value to each of the training face images is assigned as a correct label, and the model to estimate the age value of an unknown face image is trained from the training samples. In this paper, we consider the age estimation problem from the age period in which only the label of each of the training samples is the age period, such as teens or twenties. In this setting, the model has to interpolate the age values from the age period based on the similarity between the samples in the same age period. To achieve this functionality, we use Triplet Network to capture the age relationship between the face images. Then the age of each image is estimated by the linear regression. The effectiveness of the proposed approach is experimentally confirmed by using MegaAge-Asian, UTKFace, and MegaAge.
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Zhang, G., Kurita, T. (2021). Age Estimation from the Age Period by Using Triplet Network. In: Jeong, H., Sumi, K. (eds) Frontiers of Computer Vision. IW-FCV 2021. Communications in Computer and Information Science, vol 1405. Springer, Cham. https://doi.org/10.1007/978-3-030-81638-4_7
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DOI: https://doi.org/10.1007/978-3-030-81638-4_7
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