Open AccessFuzzy match index for scale‐invariant feature transform (SIFT) features with application to face recognition with weak supervision
Author(s) -
Susan Seba,
Jain Abhishek,
Sharma Aakash,
Verma Shikhar,
Jain Siddhant
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0670
Subject(s) - scale invariant feature transform , artificial intelligence , pattern recognition (psychology) , facial recognition system , computer science , fuzzy logic , feature extraction , face (sociological concept) , entropy (arrow of time) , computer vision , social science , physics , quantum mechanics , sociology
A fuzzy match index for scale‐invariant feature transform (SIFT) features is proposed in this study that cumulatively involves all the test SIFT keypoints in the decision‐making process. The new fuzzy SIFT classifier is adapted successfully for robust face recognition from complex backgrounds without using any face cropping tools and using only a single training template. The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision‐making as compared with the background features. The highlights of the authors’ work are: (i) The development of a novel highly efficient fuzzy SIFT descriptor matching tool; (ii) incorporation of feature entropy weights to highlight the contribution of facial features; (iii) application to robust face recognition from uncropped images having diverse backgrounds with a single template for each subject. The authors thus allow for weak supervision of the face recognition experiment and obtain high accuracy for 20 subjects of the CALTECH‐256 face database, 133 subjects of the labelled faces for the wild dataset and 994 subjects of the FERET database, with state‐of‐the‐art comparisons indicating the supremacy of the authors’ approach.