A method, device and equipment for evaluating the quality of a fingerprint image and a storage medium

Abstract

The application discloses a fingerprint image quality evaluation method and device, equipment and a storage medium. The method comprises the following steps: screening a target fingerprint image with a valid area from an initial fingerprint image based on a preset area integral; detecting core points in the target fingerprint image through a preset detection model, and generating core point coordinates and a fingerprint confidence; after calculating the offset of the core point coordinates, determining whether the offset exceeds an offset threshold; if yes, calculating a first core score based on the core point coordinates and the fingerprint confidence according to a punishment mechanism; if not, calculating a second core score according to the core point coordinates and the fingerprint confidence; and performing quality evaluation on the target fingerprint image according to the first core score or the second core score to obtain a quality evaluation result. Therefore, the application can solve the technical problem that the existing fingerprint quality analysis method lacks robustness and accuracy and is difficult to meet application requirements.

Description

Technical Field

[0001] The present application relates to the field of fingerprint image processing technology, and in particular to a fingerprint image quality assessment method, apparatus, device and storage medium. Background Art

[0002] Fingerprint quality testing is crucial in various applications, particularly in biometrics and security systems. Low-quality fingerprint images can lead to incorrect or unrecognized images, impacting overall system performance. Detecting and eliminating low-quality fingerprint images ensures the system uses high-quality data, improving recognition accuracy. Furthermore, low-quality fingerprints can lead to false positives (identifying incorrect fingerprints as correct) or false negatives (failure to identify the correct fingerprint). Fingerprint quality testing helps reduce these errors, improving system reliability and user experience. Furthermore, in security systems, low-quality fingerprints can be exploited by attackers for forgery or spoofing. Detecting and eliminating low-quality fingerprints enhances system security and prevents potential security vulnerabilities. Furthermore, user-submitted fingerprints of low quality may require multiple attempts to successfully register or verify, resulting in a poor user experience. Fingerprint quality testing can provide feedback during initial user submission, helping users adjust their fingerprint collection and improve success rates. Fingerprint image quality assessment is a crucial technology and a crucial component of fingerprint research and analysis.

[0003] Existing fingerprint quality analysis methods are mostly based on traditional algorithms. Not only do they have poor robustness against different devices, but they also have poor overall analysis accuracy, making it difficult to adapt to the current changing application scenarios and achieve good application results. Summary of the Invention

[0004] The present application provides a fingerprint image quality assessment method, apparatus, device and storage medium, which are used to solve the technical problem that existing fingerprint quality analysis methods lack robustness and accuracy and are difficult to meet application requirements.

[0005] In view of this, the first aspect of the present application provides a fingerprint image quality assessment method, comprising:

[0006] Based on the preset area integral, a target fingerprint image with a qualified effective area is selected from the initial fingerprint image;

[0007] Detecting the core points in the target fingerprint image using a preset detection model, and generating core point coordinates and fingerprint confidence;

[0008] After calculating the offset of the core point coordinates, determining whether the offset exceeds an offset threshold, and if so, calculating a first core score based on the core point coordinates and the fingerprint confidence based on a penalty mechanism;

[0009] if the offset does not exceed the offset threshold, calculating a second core score according to the core point coordinates and the fingerprint confidence;

[0010] performing quality assessment on the target fingerprint image according to the first core score or the second core score, to obtain a quality assessment result.

[0011] Preferably, the screening of the target fingerprint image with a qualified effective area from the initial fingerprint image based on the preset area fraction comprises:

[0012] determining an area score of the initial fingerprint image through a fingerprint test library, to obtain a preset area fraction;

[0013] judging whether the preset area fraction is greater than an area threshold, if yes, retaining the corresponding image as the target fingerprint image;

[0014] if not, determining that the effective area of the initial fingerprint image is unqualified.

[0015] Preferably, the detection of the core point in the target fingerprint image through the preset detection model and the generation of the core point coordinates and the fingerprint confidence further comprise:

[0016] if the core point of the target fingerprint image is not detected, performing fingerprint quality assessment based on the area score corresponding to the target fingerprint image, to obtain a rough assessment result.

[0017] Preferably, after the offset of the core point coordinates is calculated, it is judged whether the offset exceeds an offset threshold, if yes, calculating a first core score according to the core point coordinates and the fingerprint confidence based on a penalty mechanism, comprising:

[0018] adopting a normalization calculation method to calculate the offset corresponding to the core point coordinates, the offset comprising an x-axis offset and a y-axis offset;

[0019] if the offset exceeds the offset threshold, constructing a penalty score calculation formula based on the penalty mechanism;

[0020] substituting the core point coordinates and the fingerprint confidence into the penalty score calculation formula to perform score calculation, to obtain the first core score.

[0021] The second aspect of the present application provides a fingerprint image quality assessment device, comprising:

[0022] an area screening unit configured to screen a target fingerprint image with a qualified effective area from an initial fingerprint image based on a preset area fraction;

[0023] A core detection unit, configured to detect core points in the target fingerprint image using a preset detection model and generate core point coordinates and fingerprint confidence;

[0024] a first calculation unit, configured to, after calculating the offset of the core point coordinates, determine whether the offset exceeds an offset threshold, and if so, calculate a first core score according to the core point coordinates and the fingerprint confidence based on a penalty mechanism;

[0025] a second calculation unit, configured to calculate a second core score according to the core point coordinates and the fingerprint confidence if the offset does not exceed the offset threshold;

[0026] A quality assessment unit is configured to perform a quality assessment on the target fingerprint image according to the first core score or the second core score to obtain a quality assessment result.

[0027] Preferably, the area screening unit is specifically used to:

[0028] Determine the area score of the initial fingerprint image through the fingerprint test library to obtain a preset area score;

[0029] Determine whether the preset area integral is greater than an area threshold, and if so, retain the corresponding image as the target fingerprint image;

[0030] If not, it is determined that the effective area of ​​the initial fingerprint image is unqualified.

[0031] Preferably, it also includes:

[0032] The area evaluation unit is configured to perform fingerprint quality evaluation based on the area score corresponding to the target fingerprint image if the core point of the target fingerprint image is not detected, so as to obtain a rough evaluation result.

[0033] Preferably, the first computing unit is specifically configured to:

[0034] Calculating the offset corresponding to the core point coordinates using a normalized calculation method, wherein the offset includes an x-axis offset and a y-axis offset;

[0035] If the offset exceeds the offset threshold, a penalty score calculation formula is constructed based on the penalty mechanism;

[0036] Substituting the core point coordinates and the fingerprint confidence into the penalty score calculation formula to perform score calculation to obtain a first core score.

[0037] A third aspect of the present application provides a fingerprint image quality assessment device, the device comprising a processor and a memory;

[0038] The memory is used to store program code and transmit the program code to the processor;

[0039] The processor is configured to execute the fingerprint image quality assessment method described in the first aspect according to instructions in the program code.

[0040] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the fingerprint image quality assessment method described in the first aspect.

[0041] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0042] In the present application, a fingerprint image quality assessment method is provided, comprising: screening a target fingerprint image with a qualified effective area from an initial fingerprint image based on a preset surface integral; detecting core points in the target fingerprint image through a preset detection model, and generating core point coordinates and fingerprint confidence; after calculating the offset of the core point coordinates, determining whether the offset exceeds an offset threshold, and if so, calculating a first core score based on the core point coordinates and the fingerprint confidence based on a penalty mechanism; if the offset does not exceed the offset threshold, calculating a second core score based on the core point coordinates and the fingerprint confidence; and performing quality assessment on the target fingerprint image based on the first core score or the second core score to obtain a quality assessment result.

[0043] The fingerprint image quality assessment method provided in this application analyzes fingerprint image features from different perspectives and determines feature scores based on different calculation methods. This method, combining these scores, provides a targeted assessment of fingerprint image quality. This method focuses on the detailed characteristics of fingerprint images, better aligns with the unique characteristics of fingerprints in different scenarios, and ensures that the resulting quality assessment results are accurate and reliable. Furthermore, this process can be adapted to fingerprint image quality assessment in a variety of scenarios and exhibits good robustness. Therefore, this application addresses the technical issues that existing fingerprint quality analysis methods lack robustness and accuracy, making them difficult to meet application requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a flow chart of a fingerprint image quality assessment method provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of the structure of a fingerprint image quality assessment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0047] For easier understanding, see Figure 1 , an embodiment of a fingerprint image quality assessment method provided by the present application includes:

[0048] Step 101: Filter target fingerprint images with qualified effective areas from initial fingerprint images based on preset surface integrals.

[0049] Furthermore, step 101 includes:

[0050] Determine the area score of the initial fingerprint image through the fingerprint test library to obtain a preset area score;

[0051] Determine whether the preset area integral is greater than the area threshold. If so, retain the corresponding image as the target fingerprint image;

[0052] If not, it is determined that the effective area of ​​the initial fingerprint image is unqualified.

[0053] It should be noted that the fingerprint test library refers to the fingerprint area integral test library, which is a database used to test small-area fingerprint recognition algorithms. This test library is primarily used for capacitive sensors in mobile phones and computers; it may contain fingerprint images of different individuals and is used to evaluate and verify the performance of small-area fingerprint recognition algorithms. This embodiment uses the fingerprint test library to determine the area score corresponding to the initial fingerprint image, namely the preset area integral.

[0054] Specifically, the preset area score is calculated. If the area exceeds the preset value, the full score of the fingerprint area is used as the preset area score. If it does not exceed, the current area is divided by the product of the preset value and the full score of the area as the preset area score.

[0055] Due to the varying environments, scenarios, and methods used for fingerprint extraction, not all fingerprint images have sufficient effective area. Therefore, an area threshold is used to screen out fingerprint images with sufficient effective area before proceeding to the subsequent feature analysis and calculation process. It is understood that the area threshold can be set based on actual conditions and is not a limitation here. If the preset area score is greater than the area threshold, it indicates that the fingerprint image has a sufficiently large effective area; otherwise, it does not. If there is sufficient effective area, the fingerprint image will be retained as the target for subsequent analysis; otherwise, the fingerprint image will be preliminarily assessed as having an unqualified effective area.

[0056] Step 102: Detect the core points in the target fingerprint image using a preset detection model, and generate the core point coordinates and fingerprint confidence.

[0057] Furthermore, step 102 further includes:

[0058] If the core point of the target fingerprint image is not detected, the fingerprint quality is evaluated based on the area score corresponding to the target fingerprint image to obtain a rough evaluation result.

[0059] It should be noted that the preset detection model is a target detection network. The network structure can be designed according to the actual situation. It is not limited here. As long as it can detect the core point of the fingerprint, output the corresponding coordinates, and generate the confidence level, it will be sufficient. In this embodiment, the core point of the fingerprint image refers to the center point of the fingerprint, which is similar to a vortex-shaped point. The generated core point coordinates can be expressed as , the confidence can be expressed as .

[0060] It is understandable that fingerprint images are based on the results obtained by sensor acquisition, and not all fingerprint images can ensure that the fingerprint is centered and unbiased. Therefore, the preset detection model may not be able to detect the core point in the target fingerprint image. In this case, it is impossible to generate the corresponding core point coordinates and fingerprint confidence. In this case, the fingerprint image quality can only be roughly assessed based on the area score corresponding to the image, and the core point analysis of this fingerprint image is clearly unqualified. In addition, in the absence of a core point or when the area is insufficient, the fingerprint quality assessment score is composed of the area score. Fingerprint images with insufficient effective area are not used for subsequent analysis to prevent insufficient feature extraction and affect the analysis results.

[0061] Step 103: After calculating the offset of the core point coordinates, determine whether the offset exceeds the offset threshold. If so, calculate the first core score based on the core point coordinates and the fingerprint confidence based on a penalty mechanism.

[0062] Furthermore, step 103 includes:

[0063] The normalized calculation method is used to calculate the offset corresponding to the core point coordinates. The offset includes the x-axis offset and the y-axis offset.

[0064] If the offset exceeds the offset threshold, a penalty score calculation formula is constructed based on the penalty mechanism;

[0065] Substitute the core point coordinates and fingerprint confidence into the penalty score calculation formula to calculate the score and obtain the first core score.

[0066] It should be noted that the core point coordinates Both values ​​in need to be normalized. The normalization range selected in this embodiment is 0~0.5; that is, the x-axis coordinate and the y-axis coordinate are expressed within 0.5. The normalized value is the offset, which can be recorded as , including the x-axis offset and the y-axis offset. If the offset exceeds the offset threshold, it means that the core point has a large offset, so a penalty mechanism is needed to calculate the evaluation score of the core point to prevent the core point from exceeding the offset range.

[0067] The penalty score calculation formula based on the penalty mechanism is expressed as:

[0068]

[0069] in, 、 、 The offset The proportion and confidence level of Score the first core; 、 、 Expressed as:

[0070]

[0071]

[0072]

[0073] in, 、 are the weights corresponding to the two offsets, which can be expressed as:

[0074]

[0075]

[0076] By using the above calculation process, a first core score for quality assessment can be obtained, and quality assessment analysis of the current target fingerprint image can be achieved based on the first core score.

[0077] For example, if the core point coordinates are (0.9, 0.9), the confidence level is 0.9; then the core point coordinates are normalized to the range of 0~0.5, which is [0.9-0.5, 0.9-0.5]=(0.4, 0.4); that is, the core offset. The above formula can be used to calculate 、 ; If the full score of the score calculation process is defined as 50 points, then the proportion of x-axis offset, y-axis offset and confidence is 、 、 ; Then based on the penalty score calculation formula, the first core score can be calculated .

[0078] Step 104: If the offset does not exceed the offset threshold, a second core score is calculated based on the core point coordinates and the fingerprint confidence.

[0079] For offsets that do not exceed the offset threshold , we can also construct the calculation formula for the second core score, which is specifically expressed as:

[0080]

[0081] in, Represents the overall weight of the offset, Indicates the overall proportion of the offset, and 、 and It can be expressed as:

[0082]

[0083]

[0084]

[0085] For the case where the offset threshold is not exceeded, the second core score is mainly composed of two parts. Taking the above example as an example, if the core point coordinates are (0.9, 0.9), the confidence is 0.9; then the above formula can be used to calculate 、 and Based on the obtained parameters and the calculation formula of the second core score, the second core score can be calculated. .

[0086] Step 105: Perform quality assessment on the target fingerprint image according to the first core score or the second core score to obtain a quality assessment result.

[0087] It is understandable that either the first core score or the second core score should be determined, and the specific determination method is as described above. In order to more accurately and reliably describe the fingerprint quality, this embodiment can also perform quality assessment by combining the score and the area score. That is, the area score corresponding to the fingerprint image, i.e., the preset area score, is added to the calculated score; then, the quality of the current fingerprint image is assessed based on the total score to obtain a quality assessment result. Specifically, the first core score plus the preset area score is used as the total assessment score, or the second core score plus the preset area score is used as the total assessment score.

[0088] The fingerprint image quality assessment method provided in the embodiments of the present application analyzes fingerprint image features from different perspectives, determines feature scores based on different calculation methods, and comprehensively evaluates the fingerprint image quality using these scores. This method can focus on the detailed characteristics of the fingerprint image, better align with the unique characteristics of fingerprints in different scenarios, and ensure that the quality assessment results obtained are accurate and reliable. Furthermore, this process can be adapted to fingerprint image quality assessment in a variety of scenarios and exhibits good robustness. Therefore, the embodiments of the present application can address the technical problem that existing fingerprint quality analysis methods lack robustness and accuracy, making them difficult to meet application requirements.

[0089] For easier understanding, see Figure 2 , the present application provides an embodiment of a fingerprint image quality assessment device, comprising:

[0090] An area screening unit 201 is configured to screen target fingerprint images with qualified effective areas from the initial fingerprint images based on a preset area integral;

[0091] The core detection unit 202 is used to detect the core points in the target fingerprint image using a preset detection model and generate the core point coordinates and fingerprint confidence;

[0092] A first calculation unit 203 is configured to determine whether the offset of the core point coordinates exceeds an offset threshold after calculating the offset. If so, calculate a first core score based on the core point coordinates and the fingerprint confidence based on a penalty mechanism;

[0093] A second calculation unit 204 is configured to calculate a second core score based on the core point coordinates and the fingerprint confidence if the offset does not exceed the offset threshold;

[0094] The quality evaluation unit 205 is configured to perform quality evaluation on the target fingerprint image according to the first core score or the second core score to obtain a quality evaluation result.

[0095] Furthermore, the area screening unit 201 is specifically configured to:

[0096] Determine the area score of the initial fingerprint image through the fingerprint test library to obtain a preset area score;

[0097] Determine whether the preset area integral is greater than the area threshold. If so, retain the corresponding image as the target fingerprint image;

[0098] If not, it is determined that the effective area of ​​the initial fingerprint image is unqualified.

[0099] Furthermore, it also includes:

[0100] The area evaluation unit 206 is configured to, if the core point of the target fingerprint image is not detected, perform fingerprint quality evaluation based on the area score corresponding to the target fingerprint image, to obtain a rough evaluation result.

[0101] Further, the first calculation unit 203 is specifically configured to:

[0102] The offset amount corresponding to the core point coordinate is calculated by using a normalization calculation method, and the offset amount includes an x-axis offset amount and a y-axis offset amount.

[0103] If the offset amount exceeds an offset threshold, a penalty score calculation formula is constructed based on a penalty mechanism.

[0104] The core point coordinate and the fingerprint confidence are substituted into the penalty score calculation formula to perform score calculation, to obtain a first core score.

[0105] The application further provides a fingerprint image quality evaluation device, and the device includes a processor and a memory.

[0106] The memory is configured to store program code and transmit the program code to the processor.

[0107] The processor is configured to execute the fingerprint image quality evaluation method in the above method embodiments according to instructions in the program code.

[0108] The application further provides a computer readable storage medium, and the computer readable storage medium is configured to store program code, and the program code is configured to execute the fingerprint image quality evaluation method in the above method embodiments.

[0109] In several embodiments provided in the application, it should be understood that the disclosed apparatus and method can be implemented by other manners. For example, the apparatus embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, apparatuses or units, and can be electrical, mechanical or other forms.

[0110] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0111] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0112] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name: Read-Only Memory, English abbreviation: ROM), random access memory (full name: Random Access Memory, English abbreviation: RAM), disk or optical disk, and other media that can store program code.

[0113] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A fingerprint image quality assessment method, characterized in that: include: Based on the preset area integral, a target fingerprint image with a qualified effective area is selected from the initial fingerprint image; Detecting the core points in the target fingerprint image using a preset detection model, and generating core point coordinates and fingerprint confidence; After calculating the offset of the core point coordinates, determining whether the offset exceeds an offset threshold, and if so, calculating a first core score based on the core point coordinates and the fingerprint confidence based on a penalty mechanism; If the offset does not exceed the offset threshold, calculating a second core score based on the core point coordinates and the fingerprint confidence; A quality assessment is performed on the target fingerprint image according to the first core score or the second core score to obtain a quality assessment result.

2. The fingerprint image quality assessment method according to claim 1, characterized in that: The method of screening a target fingerprint image having a qualified effective area from the initial fingerprint image based on the preset area integral includes: Determine the area score of the initial fingerprint image through the fingerprint test library to obtain a preset area score; Determine whether the preset area integral is greater than an area threshold, and if so, retain the corresponding image as the target fingerprint image; If not, it is determined that the effective area of ​​the initial fingerprint image is unqualified.

3. The fingerprint image quality assessment method according to claim 1, characterized in that: The method of detecting the core point in the target fingerprint image by using a preset detection model and generating the core point coordinates and fingerprint confidence also includes: If the core point of the target fingerprint image is not detected, a fingerprint quality assessment is performed based on the area score corresponding to the target fingerprint image to obtain a rough assessment result.

4. The fingerprint image quality assessment method according to claim 1, characterized in that: After calculating the offset of the core point coordinates, determining whether the offset exceeds an offset threshold, and if so, calculating a first core score based on the core point coordinates and the fingerprint confidence based on a penalty mechanism, including: Calculating the offset corresponding to the core point coordinates using a normalized calculation method, wherein the offset includes an x-axis offset and a y-axis offset; If the offset exceeds the offset threshold, a penalty score calculation formula is constructed based on the penalty mechanism; Substituting the core point coordinates and the fingerprint confidence into the penalty score calculation formula to perform score calculation to obtain a first core score.

5. A fingerprint image quality assessment device, characterized in that: include: An area screening unit, configured to screen target fingerprint images with qualified effective areas from the initial fingerprint images based on a preset area integral; A core detection unit, configured to detect core points in the target fingerprint image using a preset detection model and generate core point coordinates and fingerprint confidence; a first calculation unit, configured to, after calculating the offset of the core point coordinates, determine whether the offset exceeds an offset threshold, and if so, calculate a first core score according to the core point coordinates and the fingerprint confidence based on a penalty mechanism; a second calculation unit, configured to calculate a second core score according to the core point coordinates and the fingerprint confidence if the offset does not exceed the offset threshold; A quality assessment unit is configured to perform a quality assessment on the target fingerprint image according to the first core score or the second core score to obtain a quality assessment result.

6. The fingerprint image quality assessment device according to claim 5, characterized in that: The area screening unit is specifically used for: Determine the area score of the initial fingerprint image through the fingerprint test library to obtain a preset area score; Determine whether the preset area integral is greater than an area threshold, and if so, retain the corresponding image as the target fingerprint image; If not, it is determined that the effective area of ​​the initial fingerprint image is unqualified.

7. The fingerprint image quality assessment device according to claim 5, characterized in that: Also includes: The area evaluation unit is configured to perform fingerprint quality evaluation based on the area score corresponding to the target fingerprint image if the core point of the target fingerprint image is not detected, so as to obtain a rough evaluation result.

8. The fingerprint image quality assessment device according to claim 5, characterized in that: The first computing unit is specifically configured to: Calculating the offset corresponding to the core point coordinates using a normalized calculation method, wherein the offset includes an x-axis offset and a y-axis offset; If the offset exceeds the offset threshold, a penalty score calculation formula is constructed based on the penalty mechanism; Substituting the core point coordinates and the fingerprint confidence into the penalty score calculation formula to perform score calculation to obtain a first core score.

9. A fingerprint image quality assessment device, characterized in that: The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the fingerprint image quality assessment method according to any one of claims 1 to 4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the fingerprint image quality assessment method according to any one of claims 1 to 4.

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