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

Symmetry

Se Woon Cho¹  Young Won Lee¹  Chan Sik Kim¹  Kang Ryoung Park¹  Jeon Seong Kang¹ 

[1] Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, Korea;

关键词: age estimation;    deep ResNet-152;    CNN;    optical and motion blurring;    visible light camera sensor;   

DOI  :  10.3390/sym10040108

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown