Finger vein recognition using linear Kernel Entropy Component Analysis

Based on the previous research, Kernel Entropy Component Analysis (KECA) is introduced as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face recognition. In this paper, an algorithm using KECA is proposed to merit finger vein recognition. The proposed algorithm is then compared to Principal Component Analysis (PCA) and Different types of KECA in order to determine the most appropriate one in terms of finger vein recognition.

[1]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Guo Shuxu,et al.  The Fourth Biometric - Vein Recognition , 2008 .

[3]  Leonid Mestetskiy,et al.  Face recognition using kernel entropy component analysis , 2011, Neurocomputing.

[4]  Mislav Grgic,et al.  Independent comparative study of PCA, ICA, and LDA on the FERET data set , 2005, Int. J. Imaging Syst. Technol..

[5]  Bart Jacobs,et al.  Biometrics and their use in e-passports , 2009, Image Vis. Comput..

[6]  Peng Hu,et al.  Indefinite Kernel Entropy Component Analysis , 2010, 2010 International Conference on Multimedia Technology.

[7]  K. Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Process. Lett..

[8]  Robert Jenssen,et al.  Kernel Entropy Component Analysis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Shahrel Azmin Suandi,et al.  Finger Vein Recognition Using Local Line Binary Pattern , 2011, Sensors.

[10]  Shahrel Azmin Suandi,et al.  Pose invariant face recognition for video surveillance system using kernel principle component analysis , 2012, Digital Image Processing.

[11]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  I. Jolliffe.  Principal Component Analysis , 2002 .

[13]  Shang-Hung Lin,et al.  An Introduction to Face Recognition Technology , 2000, Informing Sci. Int. J. an Emerg. Transdiscipl..