- Research Article
- Open Access
-Means Based Fingerprint Segmentation with Sensor Interoperability
© Gongping Yang et al. 2010
- Received: 29 August 2009
- Accepted: 25 March 2010
- Published: 6 May 2010
A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a -means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the -means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV). SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors.
- Fingerprint Image
- Segmentation Performance
- Nonoverlapping Block
- Fingerprint Database
- Background Block
An important preprocessing step in an automatic fingerprint recognition system is the segmentation of fingerprint images [1, 2]. Effective segmentation cannot only reduce the time of subsequent processing, but also significantly improve the reliability of feature extraction. Segmentation is the decomposition of an image into various components. A captured fingerprint image usually consists of two components, which are called the foreground and the background. The foreground is the component that originated from the contact of a fingertip with the sensor, and the noisy area usually around the borders of the image is called the background.
A number of fingerprint segmentation methods are known from literature, which can be roughly divided into block-wise methods [3–12] and pixel-wise methods [13–16]. Block-wise methods first partition a fingerprint image into nonoverlapping blocks of the same size, and then classify the blocks into foreground and background based on the extracted block-wise features. Pixel-wise methods classify pixels through the analysis of pixel-wise features. The commonly used features in fingerprint segmentation include gray-level features, orientation features, frequency domain features, and so forth.
Depending on whether the label information is used, the fingerprint segmentation methods can also be treated as unsupervised [4, 10, 14, 15, 17] and supervised ones [6–9, 11, 13, 16]. Unsupervised segmentation usually chooses an appropriate threshold for a certain feature; according to the threshold, the blocks or pixels are divided into background and foreground. Note that unsupervised segmentation does not require any label information. Supervised methods train linear or nonlinear classifiers based on labeled pixels or blocks. The classifier is then used to predict new blocks or pixels. Note that most existing methods are designed to segment fingerprints originated from a certain sensor; while the models have to be retrained as the sensor changes (It is worth noting that the various fingerprint databases are usually collected by different sensors.).
The problem of biometric sensor interoperability has attracted a lot of research interest during the past few years [18–27]. Sensor interoperability refers to "the ability of a biometric system to adapt to the raw data obtained from a variety of sensors" . Fingerprint recognition systems, which are usually designed for fingerprints originating from a certain sensor, also suffer from the sensor interoperability problem. The performances of fingerprint segmentation, enhancement, and matching may drop significantly when dealing with fingerprints collected by different sensors, due to the various image qualities, resolutions, and gray-levels.
Ross and Jain  raised the sensor interoperability problem in fingerprint recognition by matching fingerprint images originated from an optical sensor and a solid-state sensor, respectively. The experiments showed that the matching performance drastically decreases as the sensor changes. Later, the work in [19, 20] tried to improve sensor interoperability of fingerprint matcher through a Thin Plate Splines (TPS) based nonlinear calibration scheme. More works dedicated in this research field can be found in [21–27].
Note that one of the assumptions in interoperable fingerprint matching is that the fingerprint images have been properly segmented. However, fingerprint segmentation also suffers from the sensor interoperability problem. On one hand, a feature obtained from different sensors may be confused, resulting in that a block or a pixel may be considered foreground under the view of one sensor while classified as background from another sensor. On the other hand, most segmentation methods train and test on one fingerprint database collected by a certain sensor, and it is inevitable to retrain the models when dealing with other databases. Therefore, the sensor interoperability problem has to be properly addressed by designing robust fingerprint segmentation methods especially for applications with various sensors. However, to the best of our knowledge, existing works for sensor interoperability problem mainly focus on the area of fingerprint matching, leaving the sensor interoperability problem in fingerprint segmentation remains untouched. A recent work  studied the feature selection for sensor interoperable fingerprint segmentation, but it is a different problem from the one we addressed here.
This work first empirically analyzes the sensor interoperability problem in fingerprint segmentation. To effectively address this problem, we propose a -means based segmentation method called SKI, that is, segmentation based on -means for sensor Interoperability. SKI clusters foreground and background blocks of a fingerprint image based on the -means algorithm. Here a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (which are abbreviated as CMV). SKI employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The sensor interoperability and robustness of SKI are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors.
This paper is organized as follows. Section 2 raises the sensor interoperability problem through empirical studies. Section 3 proposes the SKI method, followed by experiments reported in Section 4. Finally, Section 5 concludes this work and discusses future directions.
In order to investigate the influence of various sensors on the segmentation performance, we randomly select fingerprints from a number of open databases and analyze the feature histograms and distributions of these fingerprints. Firstly, each fingerprint image is partitioned into nonoverlapping blocks of the same size, and for each block, the coherence, mean, and variance features are extracted. Then the blocks are manually labeled into two classes: foreground blocks and background blocks. Here three volunteers were asked to label the segmented blocks, and then we used a majority voting scheme to decide the ground truth labels: a block is regarded as foreground if two or more volunteers consider it as foreground; otherwise, the block is background. Finally we draw histogram and distribution for the labeled blocks of images originating from same sensor as well as from different sensors, and investigate sensor interoperability problem existing in current fingerprint segmentation methods.
The features of coherence, mean, and variance (i.e., CMV) are proposed in  to capture texture and gray-level information of local image area around a pixel. Here we modify the definition of CMV and represent each block with a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance. In detail, a fingerprint image is partitioned into nonoverlapping blocks with the same size of pixels ( is a positive integer, , usually , 12, 16), and for each block, the coherence, mean, and variance are extracted as follows.
where , , , and is the local gradient of a pixel based on Sobel operator with masks and , respectively. We set if .
where is defined the same as in (2).
In addition, we normalize the CMV values into using Min-max normalization in order to carry on the following processes.
2.2. Fingerprint Databases
FVC fingerprint database sensor list.
Low-cost Optical Sensor "Secure Desktop Scanner'' by KeyTronic
Low-cost Capacitive Sensor "TouchChip" by ST Microelectronics
Optical Sensor "DF-90" by Identicator Technology
Optical Sensor "TouchView II" by Identix
Optical Sensor "FX2000" by Biometrika
Capacitive Sensor "100 SC" by Precise Biometrics
Optical Sensor "V300" by CrossMatch
Optical Sensor "U.are.U 4000" by Digital Persona
Thermal sweeping Sensor "FingerChip FCD4B14CB" by Atmel
In our experiments, we randomly select 10 fingerprint images from each real subdatabase to construct a database containing 90 images, and the empirical studies are described in the next subsection.
2.3. Experiments and Analysis
Average number of foreground and background blocks over the 10 images selected from each subdatabase.
As mentioned before, the sensor interoperability problem should be properly addressed in order to deal with different sensors. However, existing methods are usually designed to segment fingerprints originated from the same sensor, and the model must be changed to achieve desirable performance for other sensors. For example, Chen et al.  proposed to segment fingerprints with a linear classifier trained on block-wise features of clusters degree, mean, and variance, and the parameter settings for FVC2002 DB1 and FVC2002 DB3 are [3.723, , 0.071, ] and [1.152, , 0.067, ], respectively. Therefore, facing with the sensor interoperability problem in fingerprint segmentation, a good fingerprint segmentation algorithm must be robust enough to handle the diversity produced by various sensors.
Two directions can be employed to address the sensor interoperability problem: ( ) extracting features with interoperability and ( ) designing segmentation methods with interoperability. Our recent work  follows the first strategy, and this paper proposes a new segmentation method SKI using unsupervised clustering technique.
Clustering has been attracting a lot of research interests in data mining and pattern recognition community. Unsupervised clustering explores structures in data without the need of labeled information . Indeed, fingerprint segmentation can be regarded as a two-class clustering task, and the goal is to distinguish the foreground cluster from the background one. Thus, a clustering algorithm can be performed on each fingerprint image and the segmentation can be achieved accordingly. This procedure not only avoids training a universal model for one or more fingerprint databases, but also weakens the impact of various sensors on the segmentation performance. Inspired by this, we propose SKI which is an effective clustering-based fingerprint segmentation method with sensor interoperability. The next subsection describes the -means algorithm, following by which SKI is detailed.
3.1. The -Means Algorithm
Depending on the clustering rules, existing clustering algorithms can be roughly divided into three categories: hierarchical clustering, partition based clustering, and grid-based clustering. The -means algorithm proposed by MacQueen  is a commonly used partition-based clustering method. The process of -means is presented as follows. Firstly, -means chooses the number of clusters, that is, , and determines the cluster centroids; then it assigns each data point to the nearest cluster center and recomputes the new cluster centers, and this produce is repeated until some convergence criterion is met (usually that the assignment has not changed). The main advantages of this algorithm are its simplicity and speed which allows it to run on large datasets.
In our fingerprint segmentation task, the background blocks and foreground blocks of most fingerprints (fair quality) fall into two clusters with high density (as shown in Figures 2 and 3), and the number of clusters can be directly set to be 2. Also, the statistical separability of the two clusters helps the -means algorithm to achieve good clustering performance.
We first compare SKI with state-of-the-art methods to show the effectiveness of the proposed method. Then the performance of SKI using different feature combinations is studied. In the end of this section, we show the sensor interoperability and robustness of SKI with some sample segmentation results. All the experiments are conducted on a Pentium 4 machine with a 2.0 GHz CPU and 1 GB memory.
4.1. Comparison to State-of-the-Art Methods
where denotes the number of misclassified blocks in the evaluated fingerprints, and is the total number of blocks.
Average segmentation error rates (mean ± standard deviation).
Statistic -tests results of SKI against Chen's method.
Average off-line training time and on-line segmentation time (in seconds) of SKI and Chen's method.
Off-line training time
On-line segmentation time
Average error rates of Chen's method for cross-database segmentation.
Table 6 shows that the linear classifier achieves the best performance on the subdatabase from which the classifier is trained. If a segmentation method is trained on a mixture of databases, its results might be better to some extent, but usually not as good as using the classifier to train samples and test samples from the separated subdatabase. Furthermore, it would be troublesome to retrain the classifier when using a new sensor in real production environment. In contrast, SKI avoids this problem by performing the -means algorithm on each fingerprint.
4.2. Using Different Feature Combinations
Segmentation error rates of SKI on fingerprint image FVC2000 DB3 82_1.tif using different feature combinations.
Coherence and Mean
4.3. Sample Segmentation Results
This work studies the sensor interoperability problem in fingerprint segmentation. We investigate the problem by analyzing traditional segmentation methods without sensor interoperability. We then propose a robust segmentation method called SKI to segment fingerprint images captured from different sensors. SKI is applicable to network based fingerprint recognition systems (in which fingerprint sensors may vary for different users), since it avoids adjusting threshold or retraining classifier for various sensors. To the best of our knowledge, this is the first work tackling the sensor interoperability problem in fingerprint segmentation. Experimental results also show the sensor interoperability, robustness, and effectiveness of SKI.
Most existing fingerprint segmentation methods are statistically based, which require labeled foreground and background blocks as prior knowledge. They study the feature distribution of the labeled foreground and background blocks (or pixels) to assign thresholds or train classifiers for the segmentation of new fingerprint images. It has been shown that their performances are limited when dealing with fingerprints collected by various sensors simultaneously since the feature values are usually statistically inseparable, which has been known as the sensor interoperability problem in fingerprint segmentation. The SKI method proposed in our research effectively addresses this problem by taking the following two advantages. On one hand, our method is not a statistical-based method as it applies -means algorithm on each fingerprint. We can segment a fingerprint with SKI as long as we have well-defined features to distinguish foreground blocks from background blocks for this particular image. In this work, we choose coherence, mean, and variance to describe texture information and grey-level information of a fingerprint block, respectively, which contribute to separate the foreground cluster from the background cluster even for fingerprints obtained from different sensors. On the other hand, there are naturally two clusters in the fingerprint segmentation task, that is, foreground cluster and background cluster, and as a result, we can directly set the cluster number as 2 in the -means algorithm, helping SKI to achieve high accuracy clustering with relatively small time consumption.
Note that the processing time of the -means algorithm mainly depends on the choice of initial cluster centroids. Thus, we will try to speed up SKI by automatically selecting a background block and a foreground block as the initial centroids. Besides, we find in our experiments that coherence is a feature with sensor interoperability. In so saying, under the view of coherence, the background and foreground blocks of a fair-quality fingerprint image are statistically separable, even though they are collected from various sensors. Our method takes advantages of this characteristic when dealing with the fingerprint sensor interoperability problem. Therefore, extracting other features with sensor interoperability is another promising way to further improve the performance of SKI.
The work is supported by High Technology Independent Innovation Project of Shandong Province under Grant no. 2007ZCB01030, the Natural Science Foundation of Shandong Province under Grant no. ZR2009GM003, and Independent Innovation Foundation of Shandong University. The authors would like to thank Chunxiao Ren and Liming Zhang for their helpful comments and constructive advice on structuring the paper. In addition, the authors would particularly like to thank the anonymous reviewers for helpful suggestions.
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