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Compressed sensing with MCT and I(2D) 2 PCA processing for efficient face recognition
Author(s) -
Kim Biho,
Choi Yonghwa,
Lee Minho,
Park HyungMin
Publication year - 2013
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22048
Subject(s) - computer science , facial recognition system , principal component analysis , artificial intelligence , pattern recognition (psychology) , computational complexity theory , normalization (sociology) , robustness (evolution) , feature extraction , face (sociological concept) , algorithm , social science , biochemistry , chemistry , sociology , anthropology , gene
This article describes an effective human face recognition algorithm. Even though the principle component analysis (PCA) is one of the most common feature extraction methods, it is not suitable to implement a real‐time embedded system for face recognition because large amount of computational load and memory capacity are necessary. To overcome this problem, we employ the incremental two‐directional two‐dimensional PCA (I(2D) 2 PCA) which is a combination of the (2D) 2 PCA to demand much less computational complexity than the conventional PCA and the incremental PCA (IPCA) to adapt the eigenspace only by using a new incoming sample datum without reusing of all the previous trained data. Furthermore, the modified census transform (MCT), a local normalization method useful for real‐world application and implementation in an embedded system, is adopted to address robustness to illumination variations. To achieve better recognition accuracy with less computational load, the processed features are classified by the compressive sensing approach using ℓ 2 –minimization. Experimental results on the Yale Face Database B show that the described system using the ℓ 2 –minimization‐based classification method for input data processed by the I(2D) 2 PCA and the MCT provided efficient and robust face recognition. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 133–139, 2013