Abstract
Because of its many useful applications, human age estimation has been considered in many previous studies as a soft biometrics. However, most existing methods of age estimation require a clear and focused facial image as input in order to obtain a trustworthy estimation result; otherwise, the methods might produce increased estimation error when an image of poor quality is used as input. Image blurring is one of major factors that affect estimation accuracies because it can cause a face to appear younger (i.e., reduce the age feature in the face region). Therefore, we propose a new human age estimation method that is robust even with an image that has the optical blurring effect by using symmetrical focus mask and sub-blocks for multi-level local binary pattern (MLBP). Experiment results show that the proposed method can enhance age estimation accuracy compared with the conventional system, which does not consider the effects of blurring.
Highlights
With the increase of age, facial appearance changes with the manifestation of some specific features such as wrinkles, skin spots, and corners [1]
The results indicate that the combined method of multi-level local binary pattern (MLBP) and Gabor [14] produces better estimation results compared with other methods
We evaluate the performance of the classification of blurred images into blurring groups using the MLBP measurement as explained in Section 2.3 and Figure 5
Summary
With the increase of age, facial appearance changes with the manifestation of some specific features such as wrinkles, skin spots, and corners [1]. A set of parameters is extracted for an input facial image to model the age using the trained shape and appearance models The above age estimation methods have shown good estimation results, estimation performance is affected significantly by the quality of captured face images Several factors, such as occlusion of the face region caused by hair, head pose, or the use of a facemask, in addition to blurring effects, can produce the disappearance of age information on a captured face. The distinctiveness of the face boundary is reduced This is a key problem of the AAM-based method to obtaining good estimation results.
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