By Topic

Facial Expression Recognition in the Encrypted Domain Based on Local Fisher Discriminant Analysis

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
$31 $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)
Rahulamathavan, Y. ; Sch. of Eng. & Math. Sci., City Univ. London, London, UK ; Phan, R.C.-W. ; Chambers, J.A. ; Parish, D.J.

Facial expression recognition forms a critical capability desired by human-interacting systems that aim to be responsive to variations in the human's emotional state. Recent trends toward cloud computing and outsourcing has led to the requirement for facial expression recognition to be performed remotely by potentially untrusted servers. This paper presents a system that addresses the challenge of performing facial expression recognition when the test image is in the encrypted domain. More specifically, to the best of our knowledge, this is the first known result that performs facial expression recognition in the encrypted domain. Such a system solves the problem of needing to trust servers since the test image for facial expression recognition can remain in encrypted form at all times without needing any decryption, even during the expression recognition process. Our experimental results on popular JAFFE and MUG facial expression databases demonstrate that recognition rate of up to 95.24 percent can be achieved even in the encrypted domain.

Published in:

Affective Computing, IEEE Transactions on  (Volume:4 ,  Issue: 1 )