Wavelets and Gaussian Mixture Model Approach for Gender Classification Using Fingerprints

被引：0

作者：

Rajesh, D. Gnana ^{[1
]}

Punithavalli, M. ^{[2
]}

机构：

[1] Manonmaniam Sundaranar Univ, Dept Comp Sci, Tirunelveli, Tamil Nadu, India

[2] Sri Ramakrishna Coll Arts & Sci Women, Comp Sci, Coimbatore, Tamil Nadu, India

来源：

SECOND INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN ENGINEERING AND TECHNOLOGY (ICCTET 2014) | 2014年

关键词：

gender classification; discrete wavelet transform; fingerprint; gaussian mixture model;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Gender classification is the most challenging task in forensic investigation. In this paper, a new approach to estimate gender by multiresolutional analysis of fingerprints is proposed. Discrete Wavelet Transform (DWT) is used to analyze the fingerprints in the frequency domain. The classification task is modeled by gaussian mixtures. DWT coefficients are used as features and only dominant features selected by ranking are fed into GMM for classification. This system carried out with the database of 180 persons in which 80 are females and 100 are males. The results show that the proposed system achieves 92.67% at 3rd level DWT decomposition with 16 gaussian densities.

引用

页码：522 / 525

页数：4

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