Abstract
In recent years, with the rise of deep learning and computer vision, researchers have been looking deeply into the age and gender estimation problem due to its practical influences. A lot of fields, from insurance, retails to marketing, could benefit tremendously from the presence of a reliable estimator, as it would allow companies to easily identify their customer demographics. A great number of models have been proposed, and they have achieved remarkable results. However, because of the lack of open-source, multiethnic dataset, most modern Age and Gender estimating model are trained solely based on white people with Western facial features, and thus fall short with non-Caucasian people. Therefore, in this paper, using a newly-improved Asian face database, we developed an applicable Wide ResNet model to predict the age and the gender of a person using just one image, assuming he/she comes from an Asian background. The model has shown some promising results, as it can match the performance of Microsoft’s how-old API estimator in a specific dataset.
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Acknowledgement
This work was supported by the Ho Chi Minh city Department of Science and Technology [grant numbers 1131/QD-SKHCN, 06/2018/HD-QKHCN].
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Nguyen, H., Huynh, H.T. (2019). Age and Gender Estimation of Asian Faces Using Deep Residual Network. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_18
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DOI: https://doi.org/10.1007/978-3-030-35653-8_18
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