LIFT: A new framework of learning from testing data for face recognition

Neurocomputing

Volumn 74, Issue 6, 2011, Pages 916-929

  Cao, Yuan ^a   He, Haibo ^b  

^a Stevens Institute of Technology   (United States)

^b Helen) Huang, He   (United States)

Author keywords

Data quality; Face recognition; Feature extraction; One against all; Semi supervised learning

Indexed keywords

DATA QUALITY; HYPOTHESIS TESTING; LEARNING MODELS; ONE-AGAINST-ALL; SEMI-SUPERVISED LEARNING; TESTING DATA; TRAINING EXAMPLE;

FEATURE EXTRACTION; NEURAL NETWORKS; POPULATION STATISTICS; SUPERVISED LEARNING;

FACE RECOGNITION;

ACCURACY; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FACIES; HYPOTHESIS; INFORMATION PROCESSING; LEARNING; MATHEMATICAL ANALYSIS; PERCEPTRON; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; QUALITY CONTROL; RECOGNITION; SUPPORT VECTOR MACHINE;

EID: 79751526659     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.10.015     Document Type: Article

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