Open AccessProposed face recognition system after plastic surgery
Author(s) -
Ali Amal Seralkhatem Osman,
Sagayan Vijanth,
Malik Aamir,
Aziz Azrina
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.2014.0263
Subject(s) - local binary patterns , artificial intelligence , facial recognition system , computer science , face (sociological concept) , feature (linguistics) , pattern recognition (psychology) , set (abstract data type) , computer vision , texture (cosmology) , image texture , image (mathematics) , histogram , image processing , social science , linguistics , philosophy , sociology , programming language
Plastic surgery is considered as a challenging research issue in the field of face recognition. Nevertheless, it has yet to be studied from theoretical and experimental perspectives. In this study, the authors proposed a facial recognition system for recognising faces after plastic surgery, which fuses the scores of two feature‐based and texture‐based algorithms. The feature based algorithm is the image GIST global descriptor and the texture‐based algorithm is the local binary pattern (LBP) of silence points. First, the local texture descriptor LBP was applied over a set of key points (silence points) in the face image rather than applying it over the entire face area. This proposed feature set is based on the assumption that only those LBP patterns with certain meaning, such as an edge or corner, will be useful for recognising faces that have undergone plastic surgery. The second set of features was extracted using a global descriptor, which is the GIST descriptor, to obtain a basic and a subordinate level description of the perceptual dimension. The performance of the proposed system surpassed the performance of a number of state‐of‐the‐art face recognition after plastic surgery, with a maximum verification accuracy of more than 91%.