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%.
Highlights
There are two main categories in the automatic traditional personal identification area: token-based methods that rely on personal identification such as driver licenses, passports, and other IDs; and knowledge-based methods that rely on signatures or password-protected access [1]
A quaternion model is an efficient method to utilize the information of multispectral palmprint images, as the best accuracy using three or two spectra is 98.13%, which is smaller than using all of the spectra (98.50%)
In this paper and to fully utilize the information of multispectral palmprint images, to the best of our knowledge, a quaternion model is employed for multispectral biometrics for the first time
Summary
There are two main categories in the automatic traditional personal identification area: token-based methods that rely on personal identification such as driver licenses, passports, and other IDs; and knowledge-based methods that rely on signatures or password-protected access [1]. Hao et al [20] proposed a new touchless multispectral palmprint system which is different from those in [18] and [19], but similar to the typical iris recognition system, where the user does not need to touch the sensor. These works regarded multispectral palmprint images as a kind of multi-modal biometrics and used fusion schemes on different levels, such as image level [20] and matching score level [18,19].
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