Iterative closest normal point for 3D face recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 35, Issue 2, 2013, Pages 381-397

  Mohammadzade, Hoda ^a   Hatzinakos, Dimitrios ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

3D registration; expression variation; face recognition; LDA; point correspondence; surface normal vector; Three dimensional

Indexed keywords

3D REGISTRATION; EXPRESSION VARIATION; LDA; POINT CORRESPONDENCE; SURFACE NORMALS;

DATABASE SYSTEMS; DISCRIMINANT ANALYSIS; EXPERIMENTS; ITERATIVE METHODS; THREE DIMENSIONAL; THREE DIMENSIONAL COMPUTER GRAPHICS;

FACE RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOMETRY; COMPUTER ASSISTED DIAGNOSIS; FACE; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; PHOTOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; FACE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; PATTERN RECOGNITION, AUTOMATED; PHOTOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

MLCS; MLOWN;

EID: 84871728689     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2012.107     Document Type: Article

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