Open AccessAffine‐scale invariant feature transform and two‐dimensional principal component analysis: a novel framework for affine and scale invariant face recognition
Author(s) -
A Vinay.,
Shekhar Vinay S.,
Kumar C Akshay,
S Natarajan.,
Murthy K.N. Balasubramanya
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.2014.0402
Subject(s) - affine transformation , artificial intelligence , scale invariant feature transform , affine shape adaptation , pattern recognition (psychology) , principal component analysis , harris affine region detector , computer science , computer vision , invariant (physics) , quaternion , facial recognition system , mathematics , affine coordinate system , feature extraction , affine combination , affine space , geometry , mathematical physics
Face recognition (FR) is one of the most effervescent fields of research with extensive applications that span numerous domains, and it stands resolutely as one of the most challenging problems in computer vision. The accuracy of FR systems is severely affected when two images under consideration for a match, vary in their scale and/or affine angles. The prevalent affine and scale invariant recognition systems have been predominantly developed only for objects, and hence in this study, the authors propose a novel approach for faces based on the affine‐SIFT ( ASIFT ) and two‐dimensional principal component analysis (2DPCA) techniques, to accomplish the formidable task of facial image recognition , invariant of scale and affine angles, i.e. the ability to simulate with enough accuracy, all the distortions caused by the differences in resolution and the variation of the camera optical axis direction. In the formulation of ASIFT‐2DPCA, they investigate three different variants of 2DPCA: classical 2DPCA, quaternion 2DPCA and sparse 2DPCA to gauge as to which is more effective. The authors'experimentations will demonstrate that the proposed approach can robustly handle affine and scale variations, and hence provide better accuracy and matching performance than the state‐of‐the‐art methodologies.