Title
Heterogeneous Face Recognition Using Kernel Prototype Similarities
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.
Year
DOI
Venue
2013
10.1109/TPAMI.2012.229
IEEE Trans. Pattern Anal. Mach. Intell.
Keywords
Field
DocType
face recognition,prototypes,discriminant analysis,forensics,face,kernel,biometric identification
Kernel (linear algebra),Facial recognition system,Computer vision,Infrared image,Subspace topology,Pattern recognition,Computer science,Artificial intelligence,Sampling (statistics),Linear discriminant analysis,Biometrics,Sketch
Journal
Volume
Issue
ISSN
35
6
0162-8828
Citations 
PageRank 
References 
122
2.76
26
Authors
2
Search Limit
100122
Name
Order
Citations
PageRank
Brendan Klare11313.53
Anil Jain2335073334.84