Correlated warped Gaussian processes for gender-specific age estimation

Facial age estimation is a challenging problem in computer vision. Existing methods can be classified into two categories: global and person-specific. In practice, the person-specific methods have shown better performance, however it still has some inherit problems such as over learning and mis-assignment of age estimators for unseen facial images. To fix these problems, this paper proposes correlated warped Gaussian processes (CWGP) regression for gender-specific age estimation. It uses two correlated regressors to accurately approximate the gender-specific mapping from facial features to age. Extensive experiments demonstrate the superiority of our method over state-of-the-art methods.