Off-line Palm Print Image Compression
using Wavelet Transform
Abstract
With the development of Bioinformatics,especially the palm print recognition technology, the requirement of a specify method for palm print image compression grows. To meet the increasingly severe social public security situation, the Palm print auto-recognitions system has been proposed as a key project in the Ministry of Public Security “11th Five-Year” plan. Palm print images with high resolution characteristics occupies with more storage space, which cause great inconvenience to data storage and data transmission of the actual system. Currently, palm print image compression specialized field of study is rarely approaches; many applications of palm print compression directly used the fingerprint image compression algorithm. However, as the basic distinction between palm print image and fingerprint images, those applications cannot achieve satisfactory results. Wavelet transform is one of the most important improvements base on the short time Fourier transform. And the data compression is a region growing with the wavelet technology. This paper focus on establishing a specify palm print compression method based on the characters of palm prints and building up the experimental system.
Refer to the JPEG methods, WSQ method and other wavelet compression methods, this paper process a sufficient new method based on the EZW method. It introduces a new DC level shifting scheme and set the lowest frequency coefficients to zero in EZW method, including pre-processing, wavelet transform and improved EZW method.
The experimental sample database includes 100 standard palm print images, and the evaluation sample database includes 15 images random picked up from the experimental sample database. By the PSNR test of whole image and print region, this paper’s method got higher PSNR than WSQ, JPEG and EZW methods in 10 time compression ratio. The system firstly built in MATLAB environment, then, as the final resolution, recode in C to a DLL library with great interface and efficiency.
Key Words
Biometrics identification, Offline Palm print Recognition, Wavelet Transform, image compression