Implementation plan:
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Scenario 1 : Graph Based CNN for Minutiae Extraction
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Step 1: Initially, we collect and load data from FVC2004 Fingerprint Dataset
Step 2: Then, we preprocess the images using normalization and ridge enhancement,
Step 3: Next, we extract candidate minutiae points and generate ridge structure and pixel quality feature maps.
Step 4: Next, we train the data using a Graph Based CNN by constructing graphs with nodes as ridge endings/bifurcations and edges as ridge links for mark the Minutiae and display the output
Step 5: Finally, we plot performance metrics for the following
5.1: Number of epochs vs. Accuracy (%)
5.2: Number of epochs vs. Precision (%)
5.3: Number of epochs vs. Recall (%)
5.4: Number of epochs vs. F1-score(%)
Scenario 2 :Dual CNN for Minutiae Extraction
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Step 1: Initially, we collect and load data from FVC2004 Fingerprint Dataset
Step 2: Then, we preprocess the images using normalization, ridge enhancement, binarization, thinning, ROI quality estimation, and quality index mapping.
Step 3: Next, we extract candidate minutiae points and generate ridge structure and pixel quality feature maps.
Step 4: Next, we train the data using Dual CNN with Clean Ridge CNN and Degraded Ridge CNN combined through a weighted fusion layer for mark the Minutiae and display the output
Step 5: Finally, we plot performance metrics for the following
5.1: Number of epochs vs. Accuracy (%)
5.2: Number of epochs vs. Precision (%)
5.3: Number of epochs vs. Recall (%)
5.4: Number of epochs vs. F1-score(%)
Software Requirements:
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1) Development Tool: MatlabR2023a or above
2)Operating System: Windows 10 (64-bit) or above
Dataset:
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Link: http://bias.csr.unibo.it/fvc2004/download.asp
Note:
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1) If the proposed plan does not fully align with your requirements, please provide all necessary details—including steps, parameters, models, and expected outcomes—in advance. Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming.
2) If there’s no built-in solution for what the project needs, we can always turn to reference models, customize our own, different math models or write the code ourselves to fulfil the process.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only.
5) If you have any dataset to change,kindly provide us before implementing it.
