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A deep analysis on age estimation

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10.1016/j.patrec.2015.06.006
 
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Huerta Casado, Iván
Fernandez Tena, Carles
Segura, Carlos
Hernando Pericás, Francisco JavierMés informacióMés informacióMés informació
Prati, Andrea
Document typeArticle
Defense date2015-12-15
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
The automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search optimization in large databases. Nevertheless, it becomes challenging to estimate age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity and high within-range variance. These difficulties have been recently addressed with complex and strongly hand-crafted descriptors, difficult to replicate and compare. This paper presents two novel approaches: first, a simple yet effective fusion of descriptors based on texture and local appearance; and second, a deep learning scheme for accurate age estimation. These methods have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work.
CitationHuerta, I., Fernandez, C., Segura, C., Hernando, J., Prati, A. A deep analysis on age estimation. "Pattern recognition letters", 15 Desembre 2015, vol. 68, núm. 2, p. 239-249. 
URIhttp://hdl.handle.net/2117/83918
DOI10.1016/j.patrec.2015.06.006
ISSN0167-8655
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S0167865515001683
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