JPH03266183A - Dictionary registering method for fingerprint collator - Google Patents

Abstract

PURPOSE:To improve the accuracy of a registered dictionary by counting the number of scanning position where a degree of unmatching goes less than a prescribed threshold value by performing pattern matching, and registering only a feature point corresponding to a window whose value goes less than a constant value on a dictionary as a true feature point. CONSTITUTION:A feature extraction/collation part 13 extracts feature information, and extracted feature information is sent to a dictionary evaluation part 15 sequentially. The dictionary evaluation part 15 performs the pattern matching by scanning a constant area in the neighborhood of the window by using the window of an inputted feature point, and counts the number of scanning positions in which the degree of unmatching goes less than the prescribed threshold value. Then, only the feature point corresponding to the window in which a count value goes less than the constant value is registered on the dictionary as the true feature point. In such a way, the accuracy of the dictionary can be improved.

Description

[Detailed Description of the Invention] [Summary] Regarding a dictionary registration method when registering fingerprint minutiae as a dictionary in a fingerprint matching device, the purpose of this invention is to improve the accuracy of the registered dictionary, and to improve the accuracy of the registered dictionary. A pre-processing and thinning process are performed on the input fingerprint image. A predetermined number of minutiae points are read from the fingerprint image that has undergone the above processing, and the image near the read minutiae points is used as a window to calculate the surrounding area. Perform pattern matching by scanning a certain area of The configuration is such that only the feature points are registered in the dictionary as true feature points.

[Industrial Field of Application] The present invention relates to a dictionary registration method for registering minutiae points of a fingerprint to be used as a dictionary in a fingerprint matching device.

In recent years, as computers have been introduced into a wide range of social systems, system security has become a focus of interest among those concerned. Many questions have been raised about the security of ID cards and passwords, which have been used up until now to verify one's identity when entering a computer room or using a terminal. Fingerprints, on the other hand, have two major characteristics: they are unique for everyone, and they remain unchanged throughout life, so they are considered the most powerful means of identity verification, and much research and development has been conducted on simple personal identification systems using fingerprints. There is.

[Prior Art] FIG. 3 is a conceptual diagram of the configuration of a conventional fingerprint verification device. First, the operation at the time of registration will be explained. A fingerprint pattern is detected by pressing a finger against the fingerprint sensor 1, and converted into digital data by an A/D converter (not shown) within the fingerprint sensor 1. The converted digicill data (fingerprint data) is converted into two values, “0” and “1”, by the following two values or circuit 2.
It is converted into value data and stored in the frame memory 3.

After the fingerprint data stored in the frame memory 3 is sequentially read out, it enters a feature information extraction/verification circuit 4, where feature information is extracted. Here, the characteristic information means, for example, Fig. 4 (
Refers to branch points as shown in (a) and end points as shown in (b). Fingerprints can be identified based on how many such branch points and end points there are. The feature information extraction/verification circuit 4 performs preprocessing and thinning processing on the data read from the frame memory 3. Here, preprocessing refers to removing noise contained in the fingerprint image and removing sweat glands and the like. Further, the thinning process refers to making the ridges and the like of the fingerprint image into thin lines to make the pattern easier to see. Then, characteristic information is extracted from the fingerprint image that has undergone preprocessing and thinning processing. The extracted feature information is stored in the personal feature information data file 5. The above operations are repeated for a plurality of individuals, and the individual characteristic information is stored in the personal characteristic information data file 5.

When the registration of the amount of feature information in the personal feature information pig file 5 is completed in this way, an operation for comparing the fingerprint of the individual is started. In the case of verification, when a finger (the finger used for registration in advance, for example, the index finger) is pressed against the fingerprint sensor 1, the fingerprint pattern is detected in the same way as during registration, and the A in the fingerprint sensor 1 is pressed.
The data is converted into digital data by a /D converter (not shown).

The converted digital data is subsequently converted into binary data of "0" and "1" by the binarization circuit 2 and stored in the frame memory 3.

The characteristic information extraction/verification circuit 4 reads out the fingerprint data for verification stored in the frame memory 3 and the characteristic information for each individual stored in the personal characteristic information data file 5.
Perform both matching (pattern matching). When the number of matching feature information is equal to or greater than a predetermined number, it is determined that the fingerprints match.

Here, let us consider the feature information extraction operation of the feature information extraction/verification circuit 4. The feature information extraction/verification circuit 4 first performs preprocessing (noise removal, sweat gland removal, etc.) and thinning processing on the input binary data, and then processes the fingerprint image after such processing. Extract feature information from the target.

[Problems to be Solved by the Invention] By the way, if there are many noises and sweat glands in the preprocessing stage, these noises and sweat glands will be read as pseudo feature points.

Since such pseudo minutiae are not the original minutiae of a fingerprint, if such pseudo minutiae are registered as a dictionary, the accuracy of fingerprint verification will be lost.

FIG. 5 shows true feature points, and FIGS. 6(a) to (d) show pseudo feature points. In FIG. 6, (a) shows a bridge, (b) shows a beard, (c) shows a sweat gland, and (d) shows a fissure.

As is clear from a comparison between the true feature points shown in FIG. 5 and the pseudo feature points shown in FIG. 6, the true feature points disturb the periodicity of the surrounding images. On the other hand, pseudo feature points do not disturb the periodicity of surrounding images. Therefore, Fig. 5,
If we consider windows of feature points or pseudo feature points as shown in Figure 6, scan the area around the feature points using these windows, and perform pattern matching, we find that in the case of true feature points, the In contrast to the inconsistency characteristic shown in FIG. 7 being obtained, in the case of pseudo feature points, the inconsistency degree characteristic as shown in FIG. 8 is obtained.

As is clear from FIG. 7, in the case of true feature points, there is only one portion where the degree of mismatch is close to 0. On the other hand, in the case of the pseudo minutiae shown in Fig. 8, parts where the degree of mismatch becomes small occur periodically, and parts that match the feature points of the window exhibit characteristics such that the degree of mismatch approaches O. . The broken line in the figure indicates the threshold value.

The present invention has been made in view of the above problems, and an object of the present invention is to provide a dictionary registration method for a fingerprint verification device that can improve the accuracy of registered dictionaries.

[Means for Solving the Problems] FIG. 1 is a flowchart showing the principle of the method of the present invention. The present invention manually processes a fingerprint image in step 1), performs preprocessing and thinning processing on the input fingerprint image (step 2), and extracts the number of minutiae from the processed fingerprint image to a predetermined number. reading (step 3), pattern matching is performed by scanning a certain area around the read feature point as a window (step 4), and the number of scanning positions where the degree of mismatch is smaller than a predetermined threshold is calculated. This feature is characterized in that only the feature points corresponding to the window whose counted value is below a certain value are registered in the dictionary as true feature points (step 5).

[Operation] Using the read feature point as a window, a certain area around it is scanned, pattern matching is performed, and the number of scanning positions where the degree of mismatch is smaller than a predetermined threshold is counted. If the counted value is less than a certain value, it is determined to be a true feature point and registered as a dictionary. In other cases, dictionary registration is rejected as a pseudo feature point. In this way, the accuracy of the dictionary can be improved according to the invention.

[Embodiments] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

FIG. 2 is a diagram showing an example of a system configuration for implementing the present invention.
A fingerprint verification device is shown. In the figure, 11 is a fingerprint sensor that reads a fingerprint image, and for example, a CCD is used. 1
2 is a numeric keypad for manually inputting commands when reading a fingerprint; 12 is a feature extraction/verification device that extracts characteristic information from the fingerprint image read by the fingerprint sensor 11 and also compares the read fingerprint with a dictionary. Department.

14 is a feature information storage unit that stores the feature information extracted by the feature extraction/matching unit 13; 15 is a feature information storage unit that performs pattern matching by scanning a certain area around the feature point using a window of feature points when storing the feature information; Dictionary evaluation that counts the number of scanning positions where the degree of mismatch is smaller than a predetermined threshold, and registers only the feature points corresponding to the window where the count value is below a certain value in the dictionary as true feature points. This is the part that characterizes the present invention. The operation of the circuit configured as described above will be explained as follows.

(When registering in a dictionary) First, place the finger to be registered (for example, the index finger) on the fingerprint sensor 1.
1, the fingerprint sensor 11 reads the fingerprint image of the finger and converts it into an electrical signal (analog signal). The fingerprint image signal converted into an electrical signal is sent to the feature extraction/verification unit 1.
Enter 3. Here, when a fingerprint registration command is entered from the numeric keypad 12, the feature extraction/verification section 13 starts processing the fingerprint image read from the fingerprint sensor 11. First, the feature extraction/matching section 13
The fingerprint image read in is converted into digital data by an A/D converter (not shown). Next, the fingerprint image converted into digital data is converted into binary data of "0" and "1" using a predetermined threshold value. The converted binary data is stored in a built-in frame memory (not shown).

Next, the feature extraction/verification unit 13 performs preprocessing and thinning processing on the binarized data read from the frame memory.

Here, pre-processing refers to processing such as noise removal and sweat gland removal as described above.

Thinning processing refers to making the ridges thinner to clarify the pattern. When such processing is completed, the feature extraction/verification unit 13 extracts feature information from the processed fingerprint image. The extracted feature information is sequentially sent to the dictionary evaluation section 15.

The dictionary evaluation unit 15 performs pattern matching by scanning a certain area around the input feature point using a window of the input feature point, and counts the number of scanning positions where the degree of mismatch is smaller than a predetermined threshold. Then, only the feature points corresponding to the windows whose count values are below a certain value are registered in the dictionary as true feature points. Figure 7.

As explained with reference to FIG. 8, in the case of a true feature point, if a certain area around the feature point is scanned using the feature point window and pattern matching is performed, the degree of mismatch will be a predetermined value. When the number of scanning positions that are smaller than the threshold value is counted, the counted value becomes less than or equal to a certain value.

On the other hand, in the case of a pseudo feature point, if a certain area around the feature point is scanned using the window of the feature point and pattern matching is performed, the degree of mismatch will be smaller than a predetermined threshold. When counting the number of scanning positions, the count value becomes larger than a constant value.

Therefore, the dictionary evaluation unit 15 performs evaluation by checking the degree of inconsistency of the feature points to determine whether to register the feature points as a dictionary. If it is a feature point that should be registered, it is registered in the feature extraction/verification section 13 information storage section 14 as a dictionary. In this way, according to the present invention, probable feature information can always be stored in the feature information storage section 14.

(During verification) During verification, the user places his or her finger on the fingerprint sensor 11 and then inputs his or her ID number using the numeric keypad 12.

As a result, the feature extraction/verification unit 13 determines the search range for the feature information in the feature information storage unit 14 based on the ID number, and reads it out at the time of verification, thereby narrowing down the search range.

The fingerprint image read by the fingerprint sensor 11 is binarized by the feature extraction/verification section 13. The binarized data and the feature information (dictionary) stored in the feature information storage section 14 are pattern matched (verified) by the feature extraction/verification section 13 . If the number of pattern-matched feature points is a predetermined number, the feature extraction/verification unit 13 determines that the fingerprints match.

[Effects of the Invention] As explained in detail above, according to the present invention, pattern matching is performed by scanning a certain area around the extracted feature point using a window, and the degree of mismatch is smaller than a predetermined threshold. Count the number of scan positions.

If the counted value is less than a certain value, it is determined to be a true feature point and registered as a dictionary. In other cases, dictionary registration is rejected as a pseudo feature point. In this way, the accuracy of the dictionary can be improved according to the invention.

[Brief explanation of drawings]

Fig. 1 is a flowchart showing the principle of the method of the present invention, Fig. 2 is a diagram showing an example of a system configuration implementing the present invention, Fig. 3 is a conceptual diagram of the configuration of a conventional fingerprint verification device, and Fig. 4 is a characteristic of fingerprints. Figures showing information examples; Figure 5 is a diagram showing true feature points; Figure 6 is a diagram showing pseudo feature points; Figure 7 is a diagram showing discrepancy characteristics of true feature points; Figure 8 is a diagram showing pseudo feature points. FIG. 3 is a diagram illustrating inconsistency characteristics of feature points. In FIG. 2, 11 is a fingerprint sensor, 12 is a numeric keypad 13 is a feature extraction/verification section, 14 is a feature information storage section, and 15 is a dictionary evaluation section.

Claims (1)

[Claims] A fingerprint image is input (step 1), the input fingerprint image is subjected to preprocessing and thinning processing (step 2), and a predetermined number of minutiae is determined from the processed fingerprint image. (step 3), and pattern matching is performed by scanning a certain area around the read feature point using the image as a window as a window (step 4).
), the number of scanning positions whose mismatch degree is smaller than a predetermined threshold is counted, and only the feature points corresponding to the window whose count value is below a certain value are registered in the dictionary as true feature points (step 5) A dictionary registration method for a fingerprint verification device, characterized by the following.