Yang, Huei-FangHuei-FangYangLin, Bo-YaoBo-YaoLinChang, Kuang-YuKuang-YuChangChen, Chu-SongChu-SongChen2026-04-162026-04-162015-09https://www.scopus.com/record/display.uri?eid=2-s2.0-105028553592&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/737220Automatic age estimation (AAE) from face images is a challenging problem because of large facial appearance variations resulting from a number of factors, e.g., aging and facial expressions. In this paper, we propose a generic, deep ranking model for AAE. Given a face image, our network first extracts features from the face through a scattering network (ScatNet), then reduces the feature dimension by principal component analysis (PCA), and finally predicts the age via category-wise rankers. The robustness of our approach comes from the following characteristics: (1) The scattering features are invariant to translation and small deformations; (2) the rank labels encoded in the network exploit the ordering relation among labels; and (3) the category-wise rankers perform age estimation within the same group. Our network achieves superior performance on a large-scale MORPH dataset and two expression ones, Lifespan and FACES.trueconference paper10.5244/c.29.552-s2.0-105028553592