Open AccessWithin‐component and between‐component multi‐kernel discriminating correlation analysis for colour face recognition
Author(s) -
Liu Qian,
Wang Chao
Publication year - 2017
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.2016.0294
Subject(s) - pattern recognition (psychology) , artificial intelligence , kernel (algebra) , face (sociological concept) , facial recognition system , computer science , kernel fisher discriminant analysis , feature extraction , linear discriminant analysis , kernel method , component (thermodynamics) , mathematics , support vector machine , social science , physics , combinatorics , sociology , thermodynamics
The key problem of colour face recognition technique is how to take full advantage of the colour information and extract effective discriminating features. To solve this problem, the authors propose a novel non‐linear feature extraction approach for colour face recognition, named dual multi‐kernel discriminating correlation analysis, which separately maps different colour components of face images into different non‐linear kernel spaces, and then implements multi‐kernel learning and discriminant analysis with the correlation metric not only within each colour component but also between diverse components. Then, to choose the optimum kernel space for each colour component and select the most suitable colour space for their approach, they design a kernel selection strategy and a colour space selection strategy, respectively. Experimental results in the face recognition grand challenge version 2 and labelled faces in the wilds databases validate the effectiveness of the proposed approach and two strategies.