A novel kernel discriminant feature extraction framework based on mapped virtual samples for face recognition

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2011, Pages 3005-3008

  Li, Sheng ^a   Jing, Xiaoyuan ^a,b,c   Zhang, David ^d   Yao, Yongfang ^a   Bian, Lusha ^a  

^a NANJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b WUHAN UNIVERSITY   (China)

^c NANJING UNIVERSITY   (China)

^d HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

face recognition; Kernel discriminant feature extraction framework; mapped virtual samples (MVS); MVS based kernel discriminant approaches

Indexed keywords

CLASSIFICATION PERFORMANCE; COMMON VECTORS; COMPUTATIONAL COSTS; DISCRIMINANT FEATURE EXTRACTION; FACE DATABASE; GENERALIZED DISCRIMINANT ANALYSIS; INPUT SPACE; KERNEL APPROACHES; KERNEL METHODS; KERNEL PRINCIPAL COMPONENT ANALYSIS; MVS-BASED KERNEL DISCRIMINANT APPROACHES; NON-SYMMETRIC KERNEL; PROJECTION VECTORS; VIRTUAL SAMPLE;

DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; PRINCIPAL COMPONENT ANALYSIS; VECTOR SPACES;

FACE RECOGNITION;

EID: 84863080198     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2011.6116295     Document Type: Conference Paper

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