Open AccessLocal directional mask maximum edge patterns for image retrieval and face recognition
Author(s) -
Vipparthi Santosh Kumar,
Murala Subrahmanyam,
Gonde Anil Balaji,
Jonathan Wu Q.M.
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0035
Subject(s) - local binary patterns , artificial intelligence , pattern recognition (psychology) , computer vision , computer science , robustness (evolution) , face (sociological concept) , facial recognition system , pixel , image retrieval , feature (linguistics) , feature extraction , enhanced data rates for gsm evolution , image (mathematics) , histogram , social science , biochemistry , chemistry , linguistics , philosophy , sociology , gene
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.