Paper
A Region Growing Method Based on Statistical Attributes of Infrared Images for Finger Vein Pattern Extraction
- Authors:
- E. Dermatas; M. Vlachos
- Abstract
- In this paper a method for finger vein pattern extraction from infrared images of human finger is proposed. The finger vein pattern is extracted by the execution of a two step region growing procedure, based on statistical properties of derivatives of the acquired infrared images. Initially, original image is filtered by four different Gaussian kernels (in order to take into account the different orientations of veins). Afterwards, the second partial derivatives of the obtained images are computed. Sequentially, the Hessian matrix of these images is constructed and its eigenvalues are computed in a pixel by pixel basis. The minimum eigenvalue and the absolute value of its gradient comprise the two characteristics (features) used in the two step region growing procedure which follows. The region growing procedure is restricted by statistical attributes such as the mean value and the standard deviation of the segmented regions (vein and tissue) and the mean value and the standard deviation of the gradient of the minimum eigenvalue image. Due to the occurrence of some misclassifications a final post processing step, based on morphological operations, is performed. The developed method achieves to efficiently segment the image despite of intensity variations which are evident in the original image. Moreover, an improved version of the proposed method, which uses the multidirectional response of a specially designed matched filter and its gradient as the two features used in the two stage region growing procedure, is also presented. The modified version, as experimental results show, outperforms the classic version and leads to more robust finger vein pattern extraction.
- Keywords
- Vein Pattern; Region Growing; Hessian Matrix; Eigenvalue; Gradient; Matched Filter; Morphological Postprocessing
- StartPage
- 1
- EndPage
- 15
- Doi
- 10.5963/BER0201001