1. Field of the Invention
The present invention relates generally to heterogeneous fingerprint recognition systems and, more particularly, to a method of compensating for distortion caused by fingerprint input sensors of heterogeneous fingerprint recognition systems, which is capable of compensating for distortion, caused by the heterogeneous fingerprint input sensors themselves, based on the measurement of the resolutions of a fingerprint input sensor using a Flat Artificial Finger Pattern (FAFP).
2. Description of the Related Art
In general, a fingerprint is an impression on a surface of the curves formed by the ridges on a fingertip, and fingerprints differ in shape from each other for individuals.
Accordingly, recently, fingerprint recognition has become very popular as a user authentication method for use with information devices and information services.
FIG. 1 is a block diagram of a typical fingerprint recognition system. The typical fingerprint recognition system is configured to authenticate a user in such a way that a minutia extraction module 20 extracts the minutiae of a fingerprint input through a fingerprint input sensor 10 and a matching module 30 compares the fingerprint with fingerprints previously registered in a database based on the extracted minutiae.
Meanwhile, since the current fingerprint input sensors of different manufacturers create different types of fingerprint images due to their different hardware characteristics (for example, with respect to resolution, image size, color depth or distortion rate), different functions for minutia extraction and fingerprint matching are performed to suit the characteristics of images sensed by the fingerprint input sensors of different manufacturers at the time of capturing the fingerprints.
Meanwhile, providers who provide application devices or services (the Internet or communications) using fingerprint recognition technology experience many difficulties when performing development work because the standards of commercialized fingerprint recognition devices are not harmonized with each other.
As a result, technologies for compatibility of fingerprint recognition between heterogeneous systems have been developed. To achieve compatibility of fingerprint recognition between heterogeneous systems, feature vectors insusceptible to differences in resolution and distortion between fingerprint images acquired by heterogeneous fingerprint input devices are required.
Among the technologies, the most widely known Ridge Count (RC) method uses the number of ridges existing between minutiae as feature information. This is a technology that is used in an Automatic Fingerprint Identification System (AFIS) to identify fingerprints in relation to a large-sized fingerprint database. Images input to the AFIS are chiefly images that are acquired by scanning fingerprints, impressed on paper using ink, at a high resolution using a planar scanner.
When a minutia is selected, NEC defines imaginary quadrants on the basis of the direction toward the minutia, defines a structure in which a minutia nearest to a central minutia selected from among the minutiae of each quadrant is selected, and uses the structure as a local structure for matching.
The algorithm is advantageous in that matching can be performed on a residual fingerprint, but is disadvantageous in that the structure sensitive to the direction of the minutia has low reproducibility in proportion to the number of minutiae.
In this algorithm, a coordinate system is converted using information about the direction of the minutia, which is the basis, whether a minutia adjacent to the base minutia exists in each quadrant is examined, and, if minutiae exist in all four quadrants, the ridges between the base minutia and the adjacent minutia are formed into a single group.
IBM connects two minutiae using an imaginary rectilinear line composed of five or three pixels, and information about the number of ridges is extracted by setting five or three pixels to a single segment and examining whether each segment is a ridge or valley.
Since the reliability of RC may be decreased in the case where the direction of a ridge sharply changes, information about the number of ridges is extracted only for the case where ridges are parallel with a specific direction, thereby increasing the reliability of extraction.
Information about the number of ridges is extracted by examining whether a rectilinear line connecting two minutiae is a ridge, and information about the number of ridges between a corresponding pair of minutiae is ignored in the process if a single ridge is not parallel.
Kovacs-Vajna measures the number of ridges by profiling a gray level based on a minutia located at the center of an image of extracted minutiae, and uses it for matching. Germain defines three minutiae as a triplet, and uses the number of ridges formed with respect to a formed triangle. Ratha defines other adjacent minutiae existing within a specific distance around a single minutia and a star-type structure, and uses the number of ridges existing between the minutiae for matching.
Lee searches for a click, which is certified to be the same click using a minutia-based algorithm, and expands a polygon into a concave polygonal shape. Features are extracted by expanding the polygon with respect to a minutia certified to be the same minutia and counting the number of ridges existing on a segment connecting minutiae.
This algorithm is disadvantageous in that an error occurs for a fingerprint image having different resolution and aspect ratio because the same click is searched for on a minutia basis, and the difference in distance between minutiae increases as the distance to an expansion base click increases.
As described above, attempts to extract only features robust to rotation, transition, extension and reduction, without considering the characteristics of fingerprint input sensors, and to perform fingerprint recognition between heterogeneous systems based on the extracted features have been made.
Furthermore, SC37 has performed the standardization of a biometric recognition data format so as to achieve compatibility between various biometric recognition technologies and systems. The International Labor Organization (ILO) constructed a system complying with the standard of a compatible format, and has already tested it. NIST organized a competition called Minutia Interoperability Exchange Test 2004 (MINEX04), and 15 organizations participated in the competition to determine the probability of using minutia data as information about fingerprints between heterogeneous fingerprint recognition systems, and underwent compatibility tests.
Regardless of the standardization of the data format, heterogeneous input devices have various Dot per Inch (DPI) resolutions and image sizes, therefore minutia-level matching causes a considerable reduction in recognition rate because the distortion characteristics of the sensors are different. In order to overcome the above problem, it is indispensable to perform a process of compensating for distortion, caused by a sensor, before a matching process.