By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- A new study on Machine Learning is now available. According to news reporting originating in Jiangsu, People’s Republic of China, by VerticalNews journalists, research stated, “Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way.”
Financial supporters for this research include 973 Program, National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, National Ten Thousand Talent Program of China.
The news reporters obtained a quote from the research from the Nanjing University of Science and Technology, “Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each person may have extra personalized facial characteristics, e.g., mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups.”
According to the news reporters, the research concluded: “Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.”
For more information on this research see: Personalized Age Progression with Bi-Level Aging Dictionary Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018;40(4):905-917. IEEE Transactions on Pattern Analysis and Machine Intelligence can be contacted at: Ieee Computer Soc, 10662 Los Vaqueros Circle, PO Box 3014, Los Alamitos, CA 90720-1314, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Pattern Analysis and Machine Intelligence - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34)
Our news correspondents report that additional information may be obtained by contacting X.B. Shu, Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, People’s Republic of China. Additional authors for this research include J.H. Tang, Z.C. Li, H.J. Lai, L.Y. Zhang and S.C. Yan.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TPAMI.2017.2705122. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-09), Investigators at Nanjing University of Science and Technology Describe Findings in Machine Learning (Personalized Age Progression with Bi-Level Aging Dictionary Learning), Journal of Robotics & Machine Learning, 94, ISSN: 1944-186X, BUTTER® ID: 015467729
From the newsletter Journal of Robotics & Machine Learning.
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