Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature

Abstract

Automatically and effectively estimating human ages via facial images has lots of practical applications, such as security surveillance, electronic customer relationship management and entertainment. Motivated by the fact that feature representation and recognition are two key problems in facial image based human age estimation, in this paper, we propose to employ a novel discriminative feature called Lie Algebrized Gaussians (LAG) for the representation of age images and design a two-stage approach for learning and predicting human ages. LAG is built on Gaussian Mixture Models (GMM) and is able to capture the aging manifold of the age image by preserving the Lie group manifold structure information embedded in the feature space. Given the LAG feature for each image, we estimate the human age using a two-stage approach in a coarse-to-fine fashion. In the first stage, an adaptive age group for each input image is obtained by selecting a number of neighboring age labels around the output of a global regressor. In the second stage, a local classifier is learned from the selected age classes to determine the final age of the input image. The effectiveness of our approach is evaluated on both FG-NET and MORPH benchmarks, extensive experimental results and comparisons with the state-of-the-art algorithms demonstrate the superiority of our approach for the human age estimation task.