Application Of Multi-Wavelet Theory For Fingerprint And Finger Vein In Image Processing

As a single biometric identification technology has been widely used, a multi-biometric identification technology has received more and more concern, owing to its high stability and high reliability. Multi-biometric identification technology will further enhance the recognition rate. It can use complementary information among different kinds of biometric, which make up for the defects of single mode biometric technology. The defects include trauman, loss of characteristics and bad quality of the characteristics. In this paper, the dual-mode biometric based on the fingerprint and finger vein features were studied.Image pre-processing is the first section of the automatic identification process, whose results will directly affect the following section such as edge detection, image segmentation, feature extraction and recognition accuracy. However, in the process of image acquisition and transmission, image will inevitably be subjected to a variety of noise influence. Therefore, the image pre-processing of fingerprints image and finger vein image has become a very important topic.Because of multi-resolution characteristics of wavelet transform in time-frequency domain, it is flexibility enough to extract the local features of the signal and time-varying filter. Therefore, denoising algorithm based on wavelet has become the mainstream of the existing image denoising algorithms. However, fingerprints and finger vein images with high dimension treated in this paper show that the texture information of the noodle-line is rich. It is limited for application of wavelet transform. Particularly, the ridge structure of fingerprint image was not clear and vascular collaterals of the finger vein was fuzzy in the influence of noise, which is limited for the single-wavelet theory algorithm to further improve the denoising effect. Therefore, we focus on the progress of the multi-wavelet, such as Ridgelet, Curvelet and Contourlet. All of these belong to multi-scale geometric analysis.Firstly, multi-scale geometric analysis transformation methods were studied and discussed detaily in this paper, including principles, construction methods, properties and so on. On this basis, denoising simulation of fingerprint and finger vein images was studied. Using multi-wavelet theory, the images were transformed in multi-wavelet domain. Next, we evaluated the denoising quality of image through the criteria such as Signal-to-Noise Ratio, root mean square error etc. and analyzed the corresponding denoise results of the fingerprints and finger vein image. Finally, further image segmentation, binaryzation and thinning processes are employed to the fingerprints and finger vein image processed.