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Heterogeneous Face Recognition Using Kernel Prototype Similarities

Authors
Klare, Brendan F.Jain, Anil K.
Issue Date
6월-2013
Publisher
IEEE COMPUTER SOC
Keywords
Heterogeneous face recognition; prototypes; nonlinear similarity; discriminant analysis; local descriptors; random subspaces; thermal image; infrared image; forensic sketch
Citation
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.35, no.6, pp.1410 - 1422
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume
35
Number
6
Start Page
1410
End Page
1422
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/103089
DOI
10.1109/TPAMI.2012.229
ISSN
0162-8828
Abstract
Heterogeneous face recognition (HFR) involves matching two face images from alternate imaging modalities, such as an infrared image to a photograph or a sketch to a photograph. Accurate HFR systems are of great value in various applications (e. g., forensics and surveillance), where the gallery databases are populated with photographs (e. g., mug shot or passport photographs) but the probe images are often limited to some alternate modality. A generic HFR framework is proposed in which both probe and gallery images are represented in terms of nonlinear similarities to a collection of prototype face images. The prototype subjects (i.e., the training set) have an image in each modality (probe and gallery), and the similarity of an image is measured against the prototype images from the corresponding modality. The accuracy of this nonlinear prototype representation is improved by projecting the features into a linear discriminant subspace. Random sampling is introduced into the HFR framework to better handle challenges arising from the small sample size problem. The merits of the proposed approach, called prototype random subspace (P-RS), are demonstrated on four different heterogeneous scenarios: 1) near infrared (NIR) to photograph, 2) thermal to photograph, 3) viewed sketch to photograph, and 4) forensic sketch to photograph.
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