Abstract
Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optical blurring, facial expressions, gender, etc. Motion blurring can usually be presented on face images by the movement of the camera sensor and/or the movement of the face during image acquisition. Therefore, the facial feature in captured images can be transformed according to the amount of motion, which causes performance degradation of age estimation systems. In this paper, the problem caused by motion blurring is addressed and its solution is proposed in order to make age estimation systems robust to the effects of motion blurring. Experiment results show that our method is more efficient for enhancing age estimation performance compared with systems that do not employ our method.
Highlights
Human age estimation has many useful applications, such as face recognition systems that are robust to age progress, evaluation systems of the effectiveness of advertising to customers, and systems that help prevent minors from buying alcohol, tobacco, or accessing adult websites [1,2].Because of its useful applications, age estimation has become an attractive research area, and it has been studied intensely
To overcome the problem of previous age estimation systems on poor quality images caused by motion blurring effects, we propose an age estimation method that is robust to the effects of motion blur
We proposed a new human age estimation method that is robust to the effects of motion blurring
Summary
Human age estimation has many useful applications, such as face recognition systems that are robust to age progress, evaluation systems of the effectiveness of advertising to customers, and systems that help prevent minors from buying alcohol, tobacco, or accessing adult websites [1,2].Because of its useful applications, age estimation has become an attractive research area, and it has been studied intensely. Human age has been estimated using facial images This type of method uses differences in the appearance of facial regions between old and young people. Several methods have been proposed for this problem [3,4,5,6,7,8,9], and the popular method is based on active appearance models (AAMs) [3,4,5]. This method models the shape of the human face using multiple landmark points that describe the shape of the face. The detection of multiple landmark points requires significant processing time, making it difficult to apply to real-time systems
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