Heeseung Choi

Fingerprint Matching Incorporating Ridge Features With Minutiae

This paper introduces a novel fingerprint matching algorithm using both ridge features and the conventional minutiae feature to increase the recognition performance against nonlinear deformation in fingerprints. The proposed ridge features are composed of four elements: ridge count, ridge length, ridge curvature direction, and ridge type. These ridge features have some advantages in that they can represent the topology information in entire ridge patterns existing between two minutiae and are not changed by nonlinear deformation of the finger. For extracting ridge features, we also define the ridge-based coordinate system in a skeletonized image. With the proposed ridge features and conventional minutiae features (minutiae type, orientation, and position), we propose a novel matching scheme using a breadth-first search to detect the matched minutiae pairs incrementally. Following that, the maximum score is computed and used as the final matching score of two fingerprints. Experiments were conducted for the FVC2002 and FVC2004 databases to compare the proposed method with the conventional minutiae-based method. The proposed method achieved higher matching scores. Thus, we conclude that the proposed ridge feature gives additional information for fingerprint matching with little increment in template size and can be used in conjunction with existing minutiae features to increase the accuracy and robustness of fingerprint recognition systems.

Fingerprint Image Mosaicking by Recursive Ridge Mapping

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model. The proposed method also effectively eliminates erroneous correspondences and decides how well the transformation is estimated by calculating the registration error with a normalized distance map. The proposed method consists of three phases: feature extraction, transform estimation, and mosaicking. Transform is initially estimated with matched minutia and the ridges attached to them. Unpaired ridges in the overlapping area between two images are iteratively matched by minimizing the registration error, which consists of the ridge matching error and the inverse consistency error. During the estimation, erroneous correspondences are eliminated by considering the geometric relationship between the correspondences and checking if the registration error is minimized or not. In our experiments, the proposed method was compared with three existing methods in terms of registration accuracy, image quality, minutia extraction rate, processing time, reject to fuse rate, and verification performance. The average registration error of the proposed method was less than three pixels, and the maximum error was not more than seven pixels. In a verification test, the equal error rate was reduced from 10% to 2.7% when five images were combined by our proposed method. The proposed method was superior to other compared methods in terms of registration accuracy, image quality, minutia extraction rate, and verification.

Mosaicing Touchless and Mirror-Reflected Fingerprint Images

Touchless fingerprint sensing technologies have been explored to solve problems in touch-based sensing techniques because they do not require any contact between a sensor and a finger. While they can solve problems caused by the contact of a finger, other difficulties emerge such as a view difference problem and a limited usable area due to perspective distortion. In order to overcome these difficulties, we propose a new touchless fingerprint sensing device capturing three different views at one time and a method for mosaicing these view-different images. The device is composed of a single camera and two planar mirrors reflecting side views of a finger, and it is an alternative to expensive multiple-camera-based systems. The mosaic method can composite the multiple view images by using the thin plate spline model to expand the usable area of a fingerprint image. In particular, to reduce the affect of perspective distortion, we select the regions in each view by minimizing the ridge interval variations in a final mosaiced image. Results are promising as our experiments show that mosaiced images offer 29% more true minutiae and 28% larger good quality area than one-view, unmosaiced images. Also, when the side-view images are matched to the mosaiced images, it gives more matched minutiae than matching with one-view frontal images. We expect that the proposed method can reduce the view difference problem and increase the usable area of a touchless fingerprint image. Furthermore, the proposed method can be applied to other biometric applications requiring a large template for recognition.

Fingerprint-Quality Index Using Gradient Components

Fingerprint image-quality checking is one of the most important issues in fingerprint recognition because recognition is largely affected by the quality of fingerprint images. In the past, many related fingerprint-quality checking methods have typically considered the condition of input images. However, when using the preprocessing algorithm, ridge orientation may sometimes be extracted incorrectly. Unwanted false minutiae can be generated or some true minutiae may be ignored, which can also affect recognition performance directly. Therefore, in this paper, we propose a novel quality-checking algorithm which considers the condition of the input fingerprints and orientation estimation errors. In the experiments, the 2-D gradients of the fingerprint images were first separated into two sets of 1-D gradients. Then, the shapes of the probability density functions of these gradients were measured in order to determine fingerprint quality. We used the FVC2002 database and synthetic fingerprint images to evaluate the proposed method in three ways: 1) estimation ability of quality; 2) separability between good and bad regions; and 3) verification performance. Experimental results showed that the proposed method yielded a reasonable quality index in terms of the degree of quality degradation. Also, the proposed method proved superior to existing methods in terms of separability and verification performance.

Recognizable-Image Selection for Fingerprint Recognition With a Mobile-Device Camera

This paper proposes a recognizable-image selection algorithm for fingerprint-verification systems that use a camera embedded in a mobile device. A recognizable image is defined as the fingerprint image which includes the characteristics that are sufficiently discriminating an individual from other people. While general camera systems obtain focused images by using various gradient measures to estimate high-frequency components, mobile cameras cannot acquire recognizable images in the same way because the obtained images may not be adequate for fingerprint recognition, even if they are properly focused. A recognizable image has to meet the following two conditions: First, valid region in the recognizable image should be large enough compared with other nonrecognizable images. Here, a valid region is a well-focused part, and ridges in the region are clearly distinguishable from valleys. In order to select valid regions, this paper proposes a new focus-measurement algorithm using the secondary partial derivatives and a quality estimation utilizing the coherence and symmetry of gradient distribution. Second, rolling and pitching degrees of a finger measured from the camera plane should be within some limit for a recognizable image. The position of a core point and the contour of a finger are used to estimate the degrees of rolling and pitching. Experimental results show that our proposed method selects valid regions and estimates the degrees of rolling and pitching properly. In addition, fingerprint-verification performance is improved by detecting the recognizable images.

Fingerprint mosaicking by rolling and sliding

In this paper, we propose a new scheme that a user enrolls his fingerprint images sequentially captured by rolling and sliding his finger, thus continuously contacting on the sensor. We also developed an image-fusion algorithm to mosaic the images obtained by the enrollment scheme. Conventional fusion algorithms for fingerprint images are based on large-sized sensors, and they are easily failed to combine images if there are not enough common areas among images. Our enrollment scheme assures that the common area between two sequential images is large enough to be combined even with a small-sized sensor. Experimental results show that average combined images are 1.91 times larger than a single image, and success rate for combining is 2.3 times higher than a conventional dab approach.

Fake-fingerprint detection using multiple static features

Recently, fake fingerprints have become a serious concern for the use of fingerprint recognition systems. We introduce a novel fake-fingerprint detection method that uses multiple static features. With regard to the usability of the method for field applications, we employ static features extracted from one image to determine the aliveness of fingerprints. We consider the power spectrum, histogram, directional contrast, ridge thickness, and ridge signal of each fingerprint image as representative static features. Each feature is analyzed with respect to the physiological and statistical distinctiveness of live and fake fingerprints. These features form a feature vector set and are fused at the feature level through a support vector machine classifier. For performance evaluation and comparison, a total of 7200 live images and 9000 fake images were collected using four sensors (three optical and one capacitive). Experimental results showed that proposed method achieved approximately 1.6% equal-error rate with optical-based sensors. In the case of the capacitive sensor, there was no test error when only one image was used for a decision. Based on these results, we conclude that the proposed method is a simple yet promising fake-fingerprint inspection technique in practice.

Touchless sensor capturing five fingerprint images by one rotating camera

Conventional touch-based sensors cannot capture the fingerprint images of all five fingers simultaneously due to their flat surfaces because the view of the thumb is not parallel to the other fingers. In addition, touch-based sensors have inherent difficulties, including variations in captured images due to partial contact, nonlinear distortion, inconsistent image quality, and latent images. These degrade recognition performance and user acceptance to using sensors. To overcome these difficulties, we propose a device that adopts a contact-free structure composed of a charge-coupled device (CCD) camera, rotating mirror equipped with a stepping motor, and a green light-emitting diode (LED) illuminator. The device does not make contact with any finger and captures all five fingerprint images simultaneously. We describe and discuss the structure of the proposed device in terms of four aspects: the quality of captured images, verification performance, compatibility with existing touch-based sensors, and ease of use. The experimental results show that the proposed device can capture all five fingerprint images with a high throughput (in 2.5 s), as required at the immigration control office of a country. Also, on average, a captured touchless image takes 57% of a whole rolled image whereas the image captured from a conventional touch-based sensor only takes 41% of a whole rolled image, and they have 63 and 40 true minutiae on average, respectively. Even though touchless images contain 13.18-deg rolling and 9.18-deg pitching distortion on average, 0% equal error rate (EER) is obtained by using five fingerprint images in verification stage.

Retention Esophagitis as a Significant Clinical Predictor of Progression to Esophageal Cancer in Achalasia

Background/Aims Chronic liquid and/or food stasis caused by retention esophagitis (RE) in achalasia is a notable endoscopic finding because of the presence of a thickened or whitish esophageal mucosa and histologically altered squamous hyperplasia. We aimed to identify the clinical features of RE associated with achalasia and to clarify the clinical definition of RE in achalasia as a precancerous lesion identified by analyzing biomarker expressions. Methods From 2006 to 2015, we retrospectively reviewed 37 patients with achalasia without previous treatment. Among them, 21 patients had diagnostic findings of RE (RE+) and 16 patients had no diagnostic findings of RE (RE–). Immunohistochemical staining of p53, p16, and Ki-67 was performed on the endoscopic biopsy tissues from the patients with achalasia and 10 control patients with non-obstructive dysphagia. Results The symptom duration and transit delay were significantly longer in the RE+ group than in the RE– group. We found particularly high p53 positivity rates in the RE+ group (p<0.001). The rate of p16 expression was also significantly higher in the RE+ group than in the other two groups (p=0.003). Conclusions A high p53 expression rate was more frequently found in the RE+ group than in the other two groups. RE could be a meaningful clinical feature of achalasia for predicting esophageal carcinogenesis.