Kernel functions have been very useful in data classification for the purpose of identification and verification so far. Applying such mappings first and using some methods on the mapped data such as Principal Component Analysis has been proven novel in many different areas. A lot of improvements have been proposed on PCA such as Kernel Principal Component Analysis, and Kernel Entropy Component Analysis which are known as very novel and reliable methods in face recognition and data classification. In this paper, we implemented four different Kernel mapping functions on finger database to determine the most appropriate one in terms of analyzing finger vein data using 1D-PCA. Extensive experiments have been conducted for this purpose using Polynomial, Gaussian, Exponential and Laplacian Principal Component Analysis (PCA) in 4 different examinations to determine the most significant one.
|Title of host publication||IPCV'14|
|Subtitle of host publication||the 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition|
|Place of Publication||United States|
|Publisher||World Academy of Science|
|Number of pages||5|
|Publication status||Published - 2014|
|Event||International Conference on Image Processing, Computer Vision, and Pattern Recognition (18th : 2014) - Las Vegas, Nevada, USA|
Duration: 21 Jul 2014 → 24 Jul 2014
|Conference||International Conference on Image Processing, Computer Vision, and Pattern Recognition (18th : 2014)|
|City||Las Vegas, Nevada, USA|
|Period||21/07/14 → 24/07/14|
- finger vein recognition
- Principal Component Analysis (PCA)
- Kernel Principal Component Analysis (KPCA).
Damavandinejadmonfared, S., & Varadharajan, V. (2014). Effective kernel mapping for one-dimensional Principal Component Analysis in finger vein recognition. In IPCV'14: the 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition United States: World Academy of Science.