NDLI: Markov Network-Based Unified Classifier for Face Identification

Content Provider	IEEE Xplore Digital Library
Author	Wonjun Hwang Kyungshik Roh Junmo Kim
Copyright Year	2013
Description	Author affiliation: Dept. of EE, Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea (Junmo Kim) \|\| Adv. Media Lab., Samsung Adv. Inst. of Technol., Yongin, South Korea (Wonjun Hwang; Kyungshik Roh)
Abstract	We propose a novel unifying framework using a Markov network to learn the relationship between multiple classifiers in face recognition. We assume that we have several complementary classifiers and assign observation nodes to the features of a query image and hidden nodes to the features of gallery images. We connect each hidden node to its corresponding observation node and to the hidden nodes of other neighboring classifiers. For each observation-hidden node pair, we collect a set of gallery candidates that are most similar to the observation instance, and the relationship between the hidden nodes is captured in terms of the similarity matrix between the collected gallery images. Posterior probabilities in the hidden nodes are computed by the belief-propagation algorithm. The novelty of the proposed framework is the method that takes into account the classifier dependency using the results of each neighboring classifier. We present extensive results on two different evaluation protocols, known and unknown image variation tests, using three different databases, which shows that the proposed framework always leads to good accuracy in face recognition.
Sponsorship	IEEE Comput. Soc.
Starting Page	1952
Ending Page	1959
File Size	759340
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.245
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Face Databases Face recognition Protocols Vectors Training Unified Framework Face Recognition Markov Network
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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