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Symmetry 2018, 10(4), 108; https://doi.org/10.3390/sym10040108

Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, Korea
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Received: 9 March 2018 / Revised: 9 April 2018 / Accepted: 10 April 2018 / Published: 13 April 2018
(This article belongs to the Special Issue Deep Learning-Based Biometric Technologies)
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Abstract

Recently, real-time human age estimation based on facial images has been applied in various areas. Underneath this phenomenon lies an awareness that age estimation plays an important role in applying big data to target marketing for age groups, product demand surveys, consumer trend analysis, etc. However, in a real-world environment, various optical and motion blurring effects can occur. Such effects usually cause a problem in fully capturing facial features such as wrinkles, which are essential to age estimation, thereby degrading accuracy. Most of the previous studies on age estimation were conducted for input images almost free from blurring effect. To overcome this limitation, we propose the use of a deep ResNet-152 convolutional neural network for age estimation, which is robust to various optical and motion blurring effects of visible light camera sensors. We performed experiments with various optical and motion blurred images created from the park aging mind laboratory (PAL) and craniofacial longitudinal morphological face database (MORPH) databases, which are publicly available. According to the results, the proposed method exhibited better age estimation performance than the previous methods. View Full-Text
Keywords: age estimation; deep ResNet-152; CNN; optical and motion blurring; visible light camera sensor age estimation; deep ResNet-152; CNN; optical and motion blurring; visible light camera sensor
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Kang, J.S.; Kim, C.S.; Lee, Y.W.; Cho, S.W.; Park, K.R. Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN. Symmetry 2018, 10, 108.

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