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Face recognition using Kernel eigenfaces
M. H. Yang,
N. Ahuja
, D. Kriegman
Electrical and Computer Engineering
National Center for Supercomputing Applications (NCSA)
Siebel School of Computing and Data Science
Research output
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peer-review
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Dive into the research topics of 'Face recognition using Kernel eigenfaces'. Together they form a unique fingerprint.
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Keyphrases
Principal Coordinate Analysis (PCoA)
100%
Face Recognition
100%
Eigenface
100%
Higher-order Statistics
80%
Kernel Principal Component Analysis
80%
Low-dimensional Representation
40%
Face Detection
40%
Eigenface Method
40%
Time Complexity
20%
Second-order Statistics
20%
Higher-order Correlations
20%
Second-order Correlation
20%
Vehicle Detection
20%
Statistical Dependence
20%
Memory Complexity
20%
Kernel Methods
20%
Combinatorial Explosion
20%
Recognition Detection
20%
Face Tracking
20%
Computer Science
Component Analysis
100%
Principal Components
100%
Face Recognition
100%
eigenface
100%
Higher-Order Statistics
33%
second order statistic
11%
Face Detection
11%
Kernel Method
11%
Recognition Result
11%
Vehicle Detection
11%