Journal
IEEE ACCESS
Volume 8, Issue -, Pages 17103-17120Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2967800
Keywords
Age estimation; super-resolution image reconstruction; conditional GAN; CNN
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Funding
- National Research Foundation of Korea (NRF) - Ministry of Education through the Basic Science Research Program [NRF-2018R1D1A1B07041921]
- NRF - Ministry of Science and ICT through the Basic Science Research Program [NRF-2019R1A2C1083813, NRF-2019R1F1A1041123]
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Age estimation using facial images is applicable in various fields, such as age-targeted marketing, analysis of demand and preference for goods, skin care, remote medical service, and age statistics, for describing a specific place. However, if a low-resolution camera is used to capture the images, or facial images are obtained from the subjects standing afar, the resolution of the images is degraded. In such a case, information regarding wrinkles and the texture of the face are lost, and features that are crucial for age estimation cannot be obtained. Existing studies on age estimation did not consider the degradation of resolution but used only high-resolution facial images. To overcome this limitation, this paper proposes a deep convolutional neural network (CNN)-based age estimation method that reconstructs low-resolution facial images as high-resolution images using a conditional generative adversarial network (GAN), and then uses the images as inputs. An experiment is conducted using two open databases (PAL and MORPH databases). The results demonstrate that the proposed method achieves higher accuracy in high-resolution reconstruction and age estimation than the state-of-the art methods.
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