Local directional mask maximum edge patterns for image retrieval and face recognition

Santosh Kumar Vipparthi; Subrahmanyam Murala; Anil Balaji Gonde; Q.M. Jonathan Wu

Local directional mask maximum edge patterns for image retrieval and face recognition

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Author(s): Santosh Kumar Vipparthi ¹ ; Subrahmanyam Murala ² ; Anil Balaji Gonde ³ ; Q.M. Jonathan Wu ⁴
- Affiliations: 1: Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur ;
  2: Department of Electrical Engineering, Indian Institute of Technology, Ropar ;
  3: Department of Electronics and Communication Engineering, SGGSIET, Nanded ;
  4: Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada
Source: Volume 10, Issue 3, April 2016, p. 182 – 192
DOI: 10.1049/iet-cvi.2015.0035 , Print ISSN 1751-9632, Online ISSN 1751-9640

Received 03/03/2015, Accepted 07/09/2015, Revised 08/08/2015, Published 01/04/2016

This study proposes a new feature descriptor, local directional mask maximum edge pattern, for image retrieval and face recognition applications. Local binary pattern (LBP) and LBP variants collect the relationship between the centre pixel and its surrounding neighbours in an image. Thus, LBP based features are very sensitive to the noise variations in an image. Whereas the proposed method collects the maximum edge patterns (MEP) and maximum edge position patterns (MEPP) from the magnitude directional edges of face/image. These directional edges are computed with the aid of directional masks. Once the directional edges (DE) are computed, the MEP and MEPP are coded based on the magnitude of DE and position of maximum DE. Further, the robustness of the proposed method is increased by integrating it with the multiresolution Gaussian filters. The performance of the proposed method is tested by conducting four experiments onopen access series of imaging studies-magnetic resonance imaging, Brodatz, MIT VisTex and Extended Yale B databases for biomedical image retrieval, texture retrieval and face recognition applications. The results after being investigated the proposed method shows a significant improvement as compared with LBP and LBP variant features in terms of their evaluation measures on respective databases.

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