Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection

Authors(2) :-Dr. Shubhangi D C, Maryam

Aging face recognition refers to matching the same personís faces across different ages, e.g. matching a personís older face to his (or her) younger one, which has many important practical applications such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called Local Pattern Selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way such that intra-user dissimilarity is minimized. At the second level, higher-level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH dataset (the largest face aging dataset available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods.

Authors and Affiliations

Dr. Shubhangi D C
HOD, Department of Computer Science & Engineering, VTU PG Centre, Kalaburgi, Karnataka, India
Maryam
P.G.Student, Department of Computer Science & Engineering, VTU PG Centre, Kalaburgi, Karnataka, India

Face Recognition, Aging Faces, Feature Descriptor

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 566-571
Manuscript Number : CSEIT172434
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. Shubhangi D C, Maryam, "Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.566-571, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT172434

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