title: Binary two-dimensional PCA.
abstract: Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.
authors.forename: Yanwei
authors.initials: Y
authors.lastname: Pang
authors.forename: Dacheng
authors.initials: D
authors.lastname: Tao
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yuan
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted