Facial Age Estimation by Adaptive Label Distribution Learning

Lack of sufficient and complete training data is one of the most prominent challenges in the problem of facial age estimation. Due to appearance similarity of the faces at close ages, the face images at the neighboring ages may be utilized while learning a particular age. As a result, the training images for each age are boosted without actually increase the total number of training images. This is achieved by assigning a label distribution instead of a single label of the chronological age to each face image. The label distribution should accord with the tendency of facial aging, which might be significantly different at different ages, e.g., the facial appearance during childhood and senior age generally changes faster than that during middle age. In this paper, two adaptive label distribution learning (ALDL) algorithms, IIS-ALDL and BFGS-ALDL, are proposed to automatically learn the label distributions adapted to different ages. Experimental results show that the ALDL algorithms perform remarkably better than the compared state-of-the-art algorithms.