Different Classifier Approaches Used For Fingerprint Classification

• Autorzy: Tiwari Meena , Mishra Ashish

Identyfikatory

DOI

10.15439/2022R13

• Języki publikacji: EN

Abstrakty

EN

Fingerprints play an important role in public safety and criminal investigations such as: B. Legal Investigations, Law Enforcement, Cultural Access, and Social Security. It can also help to give people a comfortable and secure life. Various gender segregation strategies have been proposed. In this article, the fingerprint algorithm uses a variety of Naive Bayes classifiers, SVM, Logistics Regression and Random Forest which they use to obtain the best results of gender segregation, a new fingerprint method can be created by Naive Bayes classifier, SVM, Logistics Regression and The Random Forest used and compiled proposed from different divisions obtained the best possible division of results by Random Forest, with 98\% accuracy compared to Naive Bayes, SVM and Logistics Regression, based on Random. The forest is the most sensitive to gender segregation.

Słowa kluczowe

EN

Naïve Bayes; SVM; Logistic Regression; Random Forest; Gender classification; Fingerprint database; Association Rule Mining

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 249--253
• Opis fizyczny: Bibliogr. 10 poz., tab., wykr.

Twórcy

autor

Tiwari Meena

• Research Scholar department of CS, SRK University Bhopal MP, India

autor

Mishra Ashish

• Professor department of CS, Gyan Ganga Institute of Technology and Science Jabalpur, India

Bibliografia

• [1] Alessandra A. Paulino et al. (2015) Latent unique mark Matching Using Descriptor-Based Correlation IEEE TRANSACTIONS ON INFORMATION FORENSICS & SECURITY, VOL. 8, NO. 1, page 31-45.
• [2] Ashish Mishra et al. (2017) A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine 0975 – 8887, Volume 169 – No.7.
• [3] Hasan, Haitham, and S. Abdul-Kareem (2013) Fingerprint image enhancement and recognition algorithms: a survey Neural Computing and Applications 23, no. 6 : 1605-1610.
• [4] Meena Tiwari, et al (2021) Development of Association Rule Mining Model for Gender Classification 1st International Conference on Computational Research and Data Analytics (ICCRDA 2020)
• [5] Neeti Kapoor et al. (2016) Sex differences in thumbprint ridge density in a central Indian population ScienceDirect, Elsevier, Egyptian Journal of Forensic Sciences 5, 23–29.
• [6] Nithin MD et al. (2011) Gender differentiation by finger ridge count among South Indian population J Forensic Leg Med,18:79–81.
• [7] Pattanawit Soanboon a et al. (2016) Determination of sex distinction from unique mark edge thickness in northeastern Thai young people ScienceDirect, Elsevier Egyptian Journal of Forensic Sciences 6, 185– 193.
• [8] S. Lee Hong et al. (2012) Neural correlates of unpredictability in behavioral patterns of wild-type and R6/2 mice communicative & integrative Biology 5:3, 1-3.
• [9] Suchita Tarare et al. (2015) Fingerprint Based Gender Classification Using DWT Transform IEEE International Conference on Computing Communication Control and Automation, 978-1-4799-6892-3/15.
• [10] Swapnil R. Shinde et al (2015) Gender Classification with KNN by Extraction of Haar Wavelet Features from Canny Shape Fingerprints IEEE International Conference on Information Processing Vishwakarma Institute of Technology. 978-1-4673-7758-4/15.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).