Open AccessHyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models
Author(s) -
Woon Cho,
Jinbeum Jang,
Andreas Koschan,
Mongi A. Abidi,
Joonki Paik
Publication year - 2016
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.24.027637
Subject(s) - hyperspectral imaging , artificial intelligence , computer science , metric (unit) , pattern recognition (psychology) , computer vision , image quality , face (sociological concept) , facial recognition system , similarity (geometry) , image (mathematics) , social science , operations management , sociology , economics
A fundamental limitation of hyperspectral imaging is the inter-band misalignment correlated with subject motion during data acquisition. One way of resolving this problem is to assess the alignment quality of hyperspectral image cubes derived from the state-of-the-art alignment methods. In this paper, we present an automatic selection framework for the optimal alignment method to improve the performance of face recognition. Specifically, we develop two qualitative prediction models based on: 1) a principal curvature map for evaluating the similarity index between sequential target bands and a reference band in the hyperspectral image cube as a full-reference metric; and 2) the cumulative probability of target colors in the HSV color space for evaluating the alignment index of a single sRGB image rendered using all of the bands of the hyperspectral image cube as a no-reference metric. We verify the efficacy of the proposed metrics on a new large-scale database, demonstrating a higher prediction accuracy in determining improved alignment compared to two full-reference and five no-reference image quality metrics. We also validate the ability of the proposed framework to improve hyperspectral face recognition.