An Improved Fingerprint Segmentation Algorithm Based on Mean and Variance

Author(s):  
Feng Wang ◽  
Xiuyou Wang ◽  
Lin Xu
2010 ◽  
Vol 159 ◽  
pp. 291-296
Author(s):  
Yan Bai Wang ◽  
Lu Tan ◽  
Nian Feng Li ◽  
Wei Liu

Introduced the fingerprint segmentation algorithm based on strength field and gradient field and designed the experimental system for the algorithm. The method is used to carry on the massive tests with fingerprint images by APC fingerprint gathering. The experimental results show that this method achieved a good fingerprint image foreground and background separation zone.


2012 ◽  
Vol 239-240 ◽  
pp. 1456-1461
Author(s):  
Hui Na Li ◽  
Jun Li Luo

In order to reduce the dependence on the images' sizes, resolutions and qualities, a self-adaptive block size fingerprint segmentation algorithm based on the gray level co-occurrence matrix (GLCM) is proposed. Firstly, the image is divided into a number of non-overlapped rectangular blocks whose size is automatically determined by the mean of the ridge distance from the spectrogram. Then the contrasts of the GLCM of each block in different directions of pixel-pair could be calculated. Since the variances of these contrasts are different for the foreground and the background, finally, the fingerprint image can be segmented correctly. Experimental results show that the proposed algorithm performs effectively in processing images gathered by various fingerprint sensors in diverse environments.


2013 ◽  
Vol 712-715 ◽  
pp. 2407-2411
Author(s):  
Feng Wang ◽  
Hong Bing Cao

Segmentation is an important part of fingerprint image preprocessing. Effective segmentation not only reduces the time of subsequent processing but also improves the reliability of feature extraction considerably. After introducing segmentation, we suggest improved mean and variance-orientation coherence gradual segmentation algorithm of fingerprint image. Morphology has been applied as preprocessing to reduce the number of classification errors. The algorithm is tested on FVC2002 database, only 0.79% of the blocks are misclassified, while the preprocessing further reduces this ratio. Experimental results verify the feasibility of this algorithm.


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