A new biologically inspired active appearance model for face age estimation by using local ordinal ranking

Authors:
Lijun Hong;Di Wen;Chi Fang;Xiaoqing Ding
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

In this paper, a new facial feature called Biologically Inspired Active Appearance Model (BIAAM) is proposed for face age estimation by using a novel age function learning algorithm, called Local Ordinal Ranking (LOR). In BIAAM, appearance variations are encoded by extracting Bio Inspired Feature from normalized shape-free images with a mean shape mask. The proposed LOR divides the training set into several groups according to age labels and applies Ordinal Hyperplanes Ranker for each group to determine the final predicting age. A multiple linear regression function is used to decide which group a query sample belongs to. Experimental evaluation on the FG-NET aging database with mean absolute error 4.18 years demonstrates that our method outperforms other state-of-the-art algorithms.