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Gender Classification from Fingerprint Using Hybrid CNN-SVM

Journal of Artificial Intelligence and Technology 2024年第1期4卷 82-87页

作者： J.Serin Keren T.Vidhya I.S.Mary Ivy Deepa V.Ebenezer a.jenefa Department of Information Technology Women’s Christian CollegeChennaiIndia Department of Computer Science and Engineering Women’s Christian CollegeChennaiIndia Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia

Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business *** biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable *** research employs fingerprints to classify gender,with the intention of being relevant for future *** methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based *** these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running *** this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main *** main goal of this study is to use CNN to extract fingerprint *** features are then sent to an SVM classifier to determine *** hybrid model’s performance measures are examined and compared to those of the conventional CNN *** a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.

关键词： digital image processing fingerprint gender classification hybrid CNN-SVM hybrid model pattern recognition

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