Wet and Wrinkled Finger Recognition Using Voting Classification and GLCM Algorithm

The automated wrinkle fingerprint recognition techniques rely on some principles from the domain of pattern recognition. There are two prevalent techniques for wrinkle fingerprint recognition. The first method is based on the pixel intensity of the wrinkles, and the second relies upon the pattern of...

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Bibliographic Details
Published in2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) pp. 1713 - 1718
Main Authors Kumar, Puneet, Sharma, Suarabh
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.12.2021
Subjects
Computational modeling
Feature extraction
Fingerprint recognition
Fingers
GLCM
Image recognition
Image segmentation
Measurement
Outs Segmentation
Voting Classification
Wet and Wrinkled
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DOI10.1109/ICAC3N53548.2021.9725564

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Summary:The automated wrinkle fingerprint recognition techniques rely on some principles from the domain of pattern recognition. There are two prevalent techniques for wrinkle fingerprint recognition. The first method is based on the pixel intensity of the wrinkles, and the second relies upon the pattern of fingerprint orientation fields. In this research paper classification method is proposed for the wet and wrinkled image recognition. The proposed method is the combination of threshold segmentation, Grey Level C0-occurrence Matrix algorithm to extract the attributes and voting classification to recognize wet image. The proposed method is executed in python and the analysis of results is done with regard to accuracy, precision and recall. The accuracy of the proposed model is achieved up to 96 percent.
DOI:10.1109/ICAC3N53548.2021.9725564