Pang Yanwei, Tao Dacheng, Yuan Yuan, Li Xuelong
IEEE Trans Syst Man Cybern B Cybern. 2008 Aug;38(4):1176-80. doi: 10.1109/TSMCB.2008.923151.
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.
快速训练和测试程序在生物特征识别研究中至关重要。传统算法，例如主成分分析（PCA），无法有效地处理大规模和高分辨率图像数据集。通过结合基于二维主成分分析（2DPCA）的图像分解的优点和基于哈尔基的快速数值计算，本技术通信首先提出了二进制二维主成分分析（B-2DPCA）。实证研究证明了B-2DPCA相对于2DPCA和二进制主成分分析的优势。
