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.
Highlights
Human age estimation based on facial images is currently a hot research topic being applied in many areas, including demographic analysis, consumer analysis, visual surveillance, and aging process analysis [1,2]
We aimed to solve the problem of degraded accuracy in capturing important facial age features, such as wrinkles, and proposed the use of a deep ResNet-152 convolutional neural network (CNN) age estimation method that was robust to various optical and motion blurring effects
No preclassification of blurring degree and direction of input images was needed, and the age estimation classifier did not need to be trained according to the preclassification results
Summary
Human age estimation based on facial images is currently a hot research topic being applied in many areas, including demographic analysis, consumer analysis, visual surveillance, and aging process analysis [1,2]. Typical features conveying age information in facial images are depth, length, and thickness of wrinkles Various methods, such as the local binary pattern (LBP), the multilevel local binary pattern (MLBP), and the Gabor filter, have been used to extract such features. In a real-world environment, various optical and motion blurring effects can occur due to movement of the camera or its user. Such effects cause problems in identifying important facial features such as wrinkles, thereby degrading estimation accuracy. To solve this problem, this study examines an age estimation method that is robust to various optical and motion blurring effects
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
