In this paper, we propose a methodology for cross matching color face images and Short Wave Infrared (SWIR) face images reliably and accurately. We first adopt a recently designed Boosted and Improved Local Gabor Pattern (ILGP) encoding and matching technique to encode face images in both visible and SWIR spectral bands. We then apply newly developed feature selection methods to prune irrelevant information in encoded data and to improve performance of the Boosted ILGP. The two newly developed feature selection methods are: (1) Genuine segment score-based thresholding and (2) AdaBoost inspired methods. We further compare the performance of the original Boosted ILGP face recognition method with the performance of the modified method that involves one of the proposed feature selection approaches. Under a general parameter set up, significant performance improvement is observed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.