Age Estimation via Grouping and Decision Fusion

Abstract

We present a novel multistage learning system, called grouping estimation fusion (GEF), for human age estimation via facial images. The GEF consists of three stages: 1) age grouping; 2) age estimation within age groups; and 3) decision fusion for final age estimation. In the first stage, faces are classified into different groups, where each group has a different age range. In the second stage, three methods are adopted to extract global features from the whole face and local features from facial components (e.g., eyes, nose, and mouth). Each global or local feature is individually utilized for age estimation in each group. Thus, several decisions (i.e., estimation results) are derived. In the third stage, we obtain the final estimated age by fusing the diverse decisions from the second stage. To create diverse decisions for fusion, we investigate multiple age grouping systems in the first stage, where each system has a different number of groups and different age ranges. Thus, various decisions can be made from the second stage, and will be delivered to the third stage for fusion. Totally, six fusion schemes (i.e., intra-system fusion, inter-system fusion, intra-inter fusion, inter-intra fusion, maximum-diversity fusion, and composite fusion) are developed and compared. The performance of the GEF system is evaluated on the Face and Gesture Recognition Research Network and the MORPH-II databases, and it outperforms the existing state-of-the-art age estimation methods by a significant margin. That is, the mean absolute errors of age estimation are reduced from 4.48 to 2.81 years on FG-NET and 3.82 to 2.97 years on MORPH-II.