Age and Gender Estimation of Asian Faces Using Deep Residual Network
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.
KeywordsDeep Learning Convolutional Neural Network ResNet Wide ResNet Age and Gender estimation
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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