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Facial age estimation is challenging due to complex dynamics in aging process, which render metric regression methods unfavorable. Rankers show better performance by exploiting the ordinal nature of ages. The difficulty of designing a ranker is that each binary classifier of a ranker has to be trained using highly unbalanced positive and negative data. This(More)
Facial age estimation is an important and challenging problem in computer vision and pattern recognition. Linear canonical correlation analysis (CCA) has been widely applied owing to low complexity, small and fixed amount of model parameters and good scalability. However, linear CCA based regression gets lower accuracy than its kernel version on the age(More)
Automatic age estimation relying on human facial images is a key technology of many real-world applications, which is still a challenging task in the computer vision field. There are three cascade modules for facial age estimation: facial aging feature extraction, dimension reduction (or feature selection) and estimation method. Many existing literatures(More)
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