Discriminating projections for estimating face age in wild images

Despite the fundamental variability of human appearance, the last several years have seen considerable advances in age estimation from images of faces. Many of these advances have been made possible by artificially removing external sources of variability-they focus on highly constrained images from datasets such as the MORPH face database and FG-NET. We introduce a novel approach to estimating age from a single “wild” image, where pose, illumination, expression, face size, and face occlusions are not managed. Our method is able to reduce the effects of variations that already exist within in image. Using pose-specific projections, we map image features into a latent space that is pose-insensitive and age-discriminative. Age estimation is then performed using a multi-class SVM. We show that our approach outperforms other published results on the Images of Groups dataset (Gallagher and Chen, 2009), which is the only age-related dataset with a non-trivial number of off-axis “wild” face images. We also show results that are competitive with recent age estimation algorithms on the mostly-frontal FG-NET dataset, and we experimentally demonstrate that our feature projections introduce insensitivity to pose.