Proceedings ArticleDOI
A Review on Gender Classification Using Association Rule Mining and Classification Based on Fingerprints
Ashish Mishra,Neelu Khare +1 more
- pp 930-934
TLDR
This study highlights the various ridge related methods like fingerprint ridge count, ridge density, ridge thickness to valley thickness ration, ridge width and fingerprint patterns used for gender identification.Abstract:
Fingerprint recognition for Gender classification method done through various techniques like Support Vector Machines (SVM), Neural Network (NN), Fuzzy- C Means (FCM). This study highlights the various ridge related methods like fingerprint ridge count, ridge density, ridge thickness to valley thickness ration, ridge width and fingerprint patterns used for gender identification.[4] This paper presents Gender classification using association rule mining and classification approach. Our aim to Gender Classification uses Data Mining Techniques Association and classification to get encourage the results.read more
Citations
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Journal ArticleDOI
A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine
Ashish Mishra,Preeti Maheshwary +1 more
TL;DR: A novel method in which the fingerprint classification can be done by the classifier used Naïve Bayes and Support vector machines efficiently reduce the search time by restricting the subsequent searching stage to either left hand thumb and right hand thumb databases.
Journal ArticleDOI
Gender classification from fused multi-fingerprint types
TL;DR: It is demonstrated that performance can be improved by classifying gender from fingerprints of fused combinations amongst the five right-hand finger types, and a fusion scheme at the abstract level, of odd number of models, trained with these fingerprint types to improve performance.
Book ChapterDOI
3D Fingerprint Gender Classification Using Deep Learning
TL;DR: This paper for the first time investigates gender classification using 3D fingerprints with high resolution imaging technology, which provides a 3D representation of the fingertip skin, and shows that the accuracy of classification based on3D fingerprints is much higher than that based on 2D fingerprints.
Journal ArticleDOI
A Robust Deep Features Enabled Touchless 3D-Fingerprint Classification System
K. C. Deepika,G. Shivakumar +1 more
TL;DR: Deep features-based Touchless 3D-Fingerprint Classification System is proposed, using a transfer deep-learning model AlexNet-CNN is used for deep feature extraction and classification, which obtains 4096 dimensional deep features.
Proceedings ArticleDOI
Gender classification of running subjects using full-body kinematics
TL;DR: The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Data Mining: Concepts and Techniques (2nd edition)
Jiawei Han,Micheline Kamber +1 more
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Book
Encyclopedia of Biometrics
Stan Z. Li,Anil K. Jain +1 more
TL;DR: Comprehensive and tutorial, the Encyclopedia of Biometrics, 2nd Edition is a practical resource for experts in the field and professionals interested in aspects of biometrics.
Journal ArticleDOI
Fingerprint classification by directional image partitioning
TL;DR: This work introduces a new approach to automatic fingerprint classification in which the directional image is partitioned into "homogeneous" connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification.