The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.
Existing fingerprint segmentation methods usually process fingerprint images captured by different sensors with the same feature or feature set. We propose to improve the fingerprint segmentation result in view of an important fact that images from different sensors have different characteristics for segmentation. Feature usability evaluation, which means to evaluate the usability of features to find the personalized feature or feature set for different sensors to improve the performance of segmentation. The need for feature usability evaluation for fingerprint segmentation is raised and analyzed as a new issue. To address this issue, we present a decision-tree-based feature-usability evaluation method, which utilizes a C4.5 decision tree algorithm to evaluate and pick the best suitable feature or feature set for fingerprint segmentation from a typical candidate feature set. We apply the novel method on the FVC2002 database of fingerprint images, which are acquired by four different respective sensors and technologies. Experimental results show that the accuracy of segmentation is improved, and time consumption for feature extraction is dramatically reduced with selected feature(s).
In an automatic finger-vein recognition system, finger-vein image quality is significant for segmentation, enhancement, and matching processes. In this paper, we propose a finger-vein image quality evaluation method using support vector machines (SVMs). We extract three features including the gradient, image contrast, and information capacity from the input image. An SVM model is built on the training images with annotated quality labels (i.e., high/low) and then applied to unseen images for quality evaluation. To resolve the class-imbalance problem in the training data, we perform oversampling for the minority class with random-synthetic minority oversampling technique. Cross-validation is also employed to verify the reliability and stability of the learned model. Our experimental results show the effectiveness of our method in evaluating the quality of finger-vein images, and by discarding low-quality images detected by our method, the overall finger-vein recognition performance is considerably improved.
In automatic fingerprint identification system, incomplete or rigid template may lead to false rejection and false matching. So, how to improve quality of the template, which is called template improvement, is important to automatic fingerprint identify system. In this paper, we propose a template improve algorithm. Based on the case-based method of machine learning and probability theory, we improve the template by deleting pseudo minutia, restoring lost genuine minutia and updating the information of minutia such as positions and directions. And special fingerprint image database is built for this work. Experimental results on this database indicate that our method is effective and quality of fingerprint template is improved evidently. Accordingly, performance of fingerprint matching is also improved stably along with the increase of using time.
Fingerprint matching is a key issue in research of an automatic fingerprint identification system. On the basis of triangulation in computational geometry, we develop a kind of method for fingerprint matching based on Delaunay Triangulation net in this paper. Through carrying on Delaunay Triangulation to the topological structure of fingerprint minutiae, minutiae with closer distance link to each other on the space according to the Delaunay criterion and form the Delaunay Triangulation net. Then look for some reference minutiae pairs correctly from the net. According to the reference minutiae pairs, match fingerprint on point pattern. The experimental results on FVC2000 indicate the validity of algorithm.
It is important to segment fingerprint image from background accurately, which could reduce time consumed on image preprocessing and improve the reliability of minutiae extraction. Methods for fingerprint image segmentation can be divided into two categories: at block level and at pixel level. This paper presents a method based on quadric surface model for fingerprint image segmentation, which belongs to method at pixel level. First, spatial distribution model of pixels based on Coherence, Mean and Variance is acquired and analyzed. 200 typical fingerprint images are selected from FVC2000 and FVC2002. The class of the pixel of these images, namely, fingerprint part or background part, is recorded manually. Coherence, Mean and Variance of each pixel are extracted and spatial distribution model of pixels is built by the use of different colors in displaying pixels of fingerprint part and pixels of background part. The model indicates that it is not linear apart and the performance of fingerprint image segmentation with a linear classifier is very limited. Second, a quadric surface formula is presented for fingerprint image segmentation and coefficients of the quadric surface formula are acquired by BP neural network. Last, in order to evaluate the performance of our method in comparison to a method using linear classifier, experiments are performed on FVC2000 DB2. Manual inspection shows that the proposed method provides accurate high-resolution segmentation results. Experimental result shows that only 0.97% of the pixels are misclassified by our method, and linear classifier misclassifies 6.8% of the pixels.