Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition

Wibowo, Moh Edi, Tjondronegoro, Dian W., Zhang, Ligang, & Himawan, Ivan (2013) Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition. In Sarkar, Sudeep & Brown, Michael (Eds.) Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision (WACV), Institute of Electrical and Electronics Engineers (IEEE), Tampa, Florida, The United States of America, pp. 46-52.

View at publisher

Abstract

To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this work, we propose to represent local models as Gaussian distributions which are learned simultaneously using the heteroscedastic probabilistic linear discriminant analysis (PLDA). Each gallery video is therefore represented as a collection of such distributions. With the PLDA, not only the within-class variations are estimated during the training, the separability between classes is also maximized leading to an improved discrimination. The heteroscedastic PLDA itself is adapted from the standard PLDA to approximate face appearance manifolds more accurately. Instead of assuming a single global within-class covariance, the heteroscedastic PLDA learns different within-class covariances specific to each local model. In the recognition phase, a probe video is matched against gallery samples through the fusion of point-to-model distances. Experiments on the Honda and MoBo datasets have shown the merit of the proposed method which achieves better performance than the state-of-the-art technique.

Impact and interest:

1 citations in Scopus
Search Google Scholar™
1 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 59154
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Computational modeling , Face, Hidden Markov models , Manifolds, Probes, Standards, Training
DOI: 10.1109/WACV.2013.6474998
ISBN: 9781467350532
ISSN: 1550-5790
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 IEEE
Deposited On: 16 Apr 2013 23:26
Last Modified: 15 Jan 2014 14:32

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page