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

Authors

  • 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

Keywords:

Face Recognition, Aging Faces, Feature Descriptor

Abstract

Aging face recognition refers to matching the same person’s faces across different ages, e.g. matching a person’s 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.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-08-31

Issue

Volume 2, Issue 4, July-August-2017

Section

Research Articles

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How to Cite

[1]
Dr. Shubhangi D C, Maryam, " Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection, IInternational 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.