Fingerprint segmentation is an important step in fingerprint recognition and is usually aimed to identify non-ridge regions and unrecoverable low quality ridge regions and exclude them as background so as to reduce th...Fingerprint segmentation is an important step in fingerprint recognition and is usually aimed to identify non-ridge regions and unrecoverable low quality ridge regions and exclude them as background so as to reduce the time expenditure of image processing and avoid detecting false features. In high and in low quality ridge regions, often are some remaining ridges which are the afterimages of the previously scanned finger and are expected to be excluded from the foreground. However, existing seg-mentation methods generally do not take the case into consideration, and often, the remaining ridge regions are falsely classified as foreground by segmentation algorithm with spurious features produced erroneously including unrecoverable regions as fore-ground. This paper proposes two steps for fingerprint segmentation aimed at removing the remaining ridge region from the fore-ground. The non-ridge regions and unrecoverable low quality ridge regions are removed as background in the first step, and then the foreground produced by the first step is further analyzed for possible remove of the remaining ridge region. The proposed method proved effective in avoiding detecting false ridges and in improving minutiae detection.展开更多
Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system （AFIS）. Accurate segmentation of a fingerprint will greatly reduce the computation time of ...Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system （AFIS）. Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and the most importantly, exclude many spurious minutiae located at the boundary of foreground. In this paper, a new fingerprint segmenta- tion algorithm is presented. First, two new features, block entropy and block gradient entropy, are proposed. Then, an AdaBoost classifier is designed to discriminate between foreground and background blocks based on these two features and five other commonly used features. The classification error rate （Err） and McNemar＇s test are used to evaluate the performance of our method. Experimental results on FVC2000, FVC2002 and FVC2004 show that our method outperforms other methods proposed in the literature both in accuracy and stability.展开更多
De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population. De-duplication is the process of removing ...De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population. De-duplication is the process of removing the instances of multiple enrollments by the same person using the person＇s biometric data. An important issue in the large-scale de-duplication applications is the speed of matching and the accuracy of the matching because the number of persons to be enrolled runs into millions. This paper presents an efficient method to improve the accuracy of fingerprint de-duplication in de-centralized manner. De-duplication accuracy decreases because of the noise present in the data, which would cause improper slap fingerprint segmentation. In this paper, an attempt is made to remove the noise present in the data by using binarization of slap fingerprint images and region labeling of desired regions with 8-adjacency neighborhood. The distinct feature of this technique is to remove the noise present in the data for an accurate slap fingerprint segmentation and improve the de-duplica- tion accuracy. Experimental results demonstrate that the fingerprint segmentation rate and de-duplication accuracy are improved significantly.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60373023), and the Science and Technology Research Foundation of Hunan City University (No. 20057306), China
文摘Fingerprint segmentation is an important step in fingerprint recognition and is usually aimed to identify non-ridge regions and unrecoverable low quality ridge regions and exclude them as background so as to reduce the time expenditure of image processing and avoid detecting false features. In high and in low quality ridge regions, often are some remaining ridges which are the afterimages of the previously scanned finger and are expected to be excluded from the foreground. However, existing seg-mentation methods generally do not take the case into consideration, and often, the remaining ridge regions are falsely classified as foreground by segmentation algorithm with spurious features produced erroneously including unrecoverable regions as fore-ground. This paper proposes two steps for fingerprint segmentation aimed at removing the remaining ridge region from the fore-ground. The non-ridge regions and unrecoverable low quality ridge regions are removed as background in the first step, and then the foreground produced by the first step is further analyzed for possible remove of the remaining ridge region. The proposed method proved effective in avoiding detecting false ridges and in improving minutiae detection.
基金Acknowledgements This paper was supported by the National High Technology Research and Development Program of China （2008AA01Z411）, the National Natural Science Foundation of China （Grant Nos. 60902083, 60803151, and 60875018）, the Beijing Natural Science Fund （4091004）, and the Fundamental Research Funds for the Central Universities （K50510100003）.
文摘Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system （AFIS）. Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and the most importantly, exclude many spurious minutiae located at the boundary of foreground. In this paper, a new fingerprint segmenta- tion algorithm is presented. First, two new features, block entropy and block gradient entropy, are proposed. Then, an AdaBoost classifier is designed to discriminate between foreground and background blocks based on these two features and five other commonly used features. The classification error rate （Err） and McNemar＇s test are used to evaluate the performance of our method. Experimental results on FVC2000, FVC2002 and FVC2004 show that our method outperforms other methods proposed in the literature both in accuracy and stability.
文摘De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population. De-duplication is the process of removing the instances of multiple enrollments by the same person using the person＇s biometric data. An important issue in the large-scale de-duplication applications is the speed of matching and the accuracy of the matching because the number of persons to be enrolled runs into millions. This paper presents an efficient method to improve the accuracy of fingerprint de-duplication in de-centralized manner. De-duplication accuracy decreases because of the noise present in the data, which would cause improper slap fingerprint segmentation. In this paper, an attempt is made to remove the noise present in the data by using binarization of slap fingerprint images and region labeling of desired regions with 8-adjacency neighborhood. The distinct feature of this technique is to remove the noise present in the data for an accurate slap fingerprint segmentation and improve the de-duplica- tion accuracy. Experimental results demonstrate that the fingerprint segmentation rate and de-duplication accuracy are improved significantly.
