Open AccessThis article is
- freely available
Multispectral Palmprint Recognition Using a Quaternion Matrix
Bio-Computing Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
The Institute of Automation of Heilongjiang Academy of Sciences, Harbin 150090, China
Department of Computer Science, Baoji University of Arts and Science, Xi’an 721013, China
* Author to whom correspondence should be addressed.
Received: 20 February 2012; in revised form: 21 March 2012 / Accepted: 21 March 2012 / Published: 10 April 2012
Abstract: Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.
Keywords: multispectral palmprints; quaternion; PCA; DWT
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Xu, X.; Guo, Z.; Song, C.; Li, Y. Multispectral Palmprint Recognition Using a Quaternion Matrix. Sensors 2012, 12, 4633-4647.
Xu X, Guo Z, Song C, Li Y. Multispectral Palmprint Recognition Using a Quaternion Matrix. Sensors. 2012; 12(4):4633-4647.
Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng. 2012. "Multispectral Palmprint Recognition Using a Quaternion Matrix." Sensors 12, no. 4: 4633-4647.