By Topic

Facial expression invariant person recognition using feature level fusion of visual and thermal images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Mrinal Kanti Bhowmik ; Department of Computer Science and Engineering, Tripura University (A Central University), Suryamaninagar - 799022, India ; Debotosh Bhattacharjee ; Dipak Kumar Basu ; Mita Nasipuri

In this paper feature level fusion scheme of visual and thermal images in wavelet domain is being analysed. Here, Daubechies wavelet transform, termed as db4, coefficients from visual and corresponding coefficients computed in the same manner from thermal images are combined to get fused coefficients. After decomposition up to fifth level (Level 5) fusion of coefficients is done. At the time of generating fused images of coefficients, two different approaches (absolute maximum and absolute minimum) have been followed. Inverse Daubechies wavelet transform of those coefficients gives us fused face images. The main advantage of using wavelet transform is that it is well suited to manage different image resolutions and after decomposition, the high and low frequency sub-band images can be separated. Therefore, the reconstructed fused images have the information of low and high frequencies. These fused images have been passed logarithmic domain (log-ICA) for reduction of dimensions, which is suitable for face images varying with expressions. Finally, those reduced fused images are classified using a multi-layer perceptron. For experiments, IRIS Thermal/Visual Face Database was used. Experimental results show that the performance of the approach presented here gives an average success rate of 94.81%.

Published in:

Information and Communication Technologies (WICT), 2011 World Congress on

Date of Conference:

11-14 Dec. 2011