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Joint feature learning for face recognition
Jiwen Lu
, Venice Erin Liong
, Gang Wang
,
Pierre Moulin
Electrical and Computer Engineering
Beckman Institute for Advanced Science and Technology
Statistics
Information Trust Institute
Coordinated Science Lab
Research output
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peer-review
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Keyphrases
Face Recognition
100%
Joint Feature Learning
100%
Feature Representation
66%
Face Area
66%
Face Representation
66%
Physical Characteristics
33%
Learned Features
33%
Learning Methods
33%
Learning Approaches
33%
Learning Model
33%
Recognition Performance
33%
Face Patches
33%
Face Database
33%
Deep Learning Architectures
33%
New Joint
33%
Face Recognition System
33%
Hierarchical Information
33%
Position-specific
33%
Discriminative Information
33%
Feature Dictionary
33%
Conventional Features
33%
Local Binary Pattern Features
33%
Gabor Features
33%
Unsupervised Feature Learning
33%
Hierarchical Feature Representation
33%
Feature Descriptor
33%
Spatial Pooling
33%
Feature Projection Matrix
33%
Feature Projection
33%
Representative Power
33%
Computer Science
Representation Learning
100%
Face Recognition
100%
Face Representation
50%
Experimental Result
25%
Learning Approach
25%
Physical Characteristic
25%
Recognition System
25%
Recognition Performance
25%
Deep Architecture
25%
Related Feature
25%
Projection Matrix
25%
feature descriptor
25%
Gabor feature
25%
Learned Feature
25%