Facial age estimation with images in the wild

In this paper, we investigate facial age estimation with images in the wild. We aim to utilize images from the Internet to alleviate the problem of imbalance in age distribution. First, we crawl 14,283 images with their context from Wikipedia and infer age labels from the context for each image. After face detection, facial landmark detection and alignment, we build a set of images for facial age estimation, containing 9,456 faces with significant variations. Then, we exploit cost-sensitive learning algorithms including biased penalties SVM and Random forests for age estimation, using images in the wild as the training set. We propose to use the Gaussian function to determine varied misclassification costs. Conducted on two public aging datasets, the within-database experiments illustrate the performance improvement with the introduction of images in the wild. Furthermore, our cross-database experiments validate the generalization capability of proposed cost-sensitive age estimator.