Two-dimensional face recognition algorithms in the frequency domain

• Published in: 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) pp. 1 - 4
• Main Authors: Zeytunlu, A. S., Ahmad, M. O., Swamy, M. N. S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2012
• Subjects: (2D)2PCA; 2DPCA; Accuracy; Classification algorithms; Covariance matrix; DiaPCA; Face recognition; Fourier magnitudes; PCA; Principal component analysis; Training
• ISBN: 1467314315; 9781467314312
• ISSN: 0840-7789
• DOI: 10.1109/CCECE.2012.6335036

Principal component analysis (PCA), well-known for its compaction capability and robustness against noise, is a widely used technique for face recognition. However, it has major drawbacks: (i) losing image details, (ii) having a large time complexity and (iii) suffering from adverse effect of intra-class pose variations. To overcome the last drawback in PCA, Fourier magnitude (FM-PCA) has been proposed in which Fourier magnitudes have been used for feature extraction. Furthermore, to address the other two drawbacks, two-dimensional PCA (2DPCA) algorithms have been proposed. In this paper, to overcome the problems (i) and (ii) in FM-PCA and the problem (iii) in 2DPCA algorithms, Fourier magnitude 2DPCA algorithms which incorporate the advantages of FM-PCA and 2DPCA algorithms are developed. Extensive simulations on the ORL database confirm the effectiveness of the proposed algorithms in providing higher accuracy over PCA, FM-PCA and 2DPCA algorithms with a much smaller complexity compared to that of FM-PCA.