caa a22 4500 605447470 CHVBK 20210128100132.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-013-1815-z doi (NATIONALLICENCE)springer-10.1007/s11042-013-1815-z Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature [Elektronische Daten] [Chunlong Hu, Liyu Gong, Tianjiang Wang, Qi Feng] 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. Springer Science+Business Media New York, 2013 Human age estimation nationallicence Manifold learning nationallicence Gaussian mixture models nationallicence Lie Algebrized Gaussians nationallicence Regression and classification nationallicence Hu Chunlong School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China aut Gong Liyu Eedoo Inc, 100085, Beijing, China aut Wang Tianjiang School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China aut Feng Qi School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 4139-4159 1380-7501 74:11<4139 2015 74 11042 https://doi.org/10.1007/s11042-013-1815-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1815-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Hu Chunlong School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China aut NATIONALLICENCE 700 1- Gong Liyu Eedoo Inc, 100085, Beijing, China aut NATIONALLICENCE 700 1- Wang Tianjiang School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China aut NATIONALLICENCE 700 1- Feng Qi School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 4139-4159 1380-7501 74:11<4139 2015 74 11042