Automatic estimation of human facial age is an interesting yet challenging topic appearing in recent years. Since different people might age in different ways, solving the problem of age estimation involves two semantic labels: identity and age. In this paper, aging face images are organized in a third-order tensor according to both identity and age. Due to the difficulty in data collection, the aging pattern for each person in the training set is always incomplete. Therefore, the tensor contains a large amount of missing values. Through a series of multilinear subspace analysis algorithms operating on tensor with missing values, the aging pattern contained in the training aging images can be iteratively learned and be used to predict the age of a given test image. In the experiment, the proposed method not only outperforms the existing algorithms, but also exceeds the human ability in age estimation.
['Xin Geng', 'Kate Smith-Miles']
Facial age estimation by multilinear subspace analysis