报告详情
Human Gender Classification Using Fingerprint Images
编号：28
访问权限：仅限参会人
更新：2024-07-31 14:34:35 浏览：323次
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报告开始：暂无开始时间（Asia/Bangkok）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
In forensic investigations, gender identification plays a vital role in helping to identify individuals involved in criminal activities. Accurate gender identification is hindered by problems such as incomplete or degraded biological samples and limited data. The aim is to develop an accurate deep learning model of gender classification using altered low-quality images, to investigate the impact of various finger types, and to apply fingerprint reconstruction techniques. The Sokoto Coventry Fingerprint Dataset is utilized, featuring diverse fingerprint images with obliteration artificial modifications. Differences in ridge density between male and female fingerprints, with females having a higher density, have been identified as a key finding, which helps to identify the gender accurately. In demonstrating its potential for forensic use, the gender classification model achieved an excellent accuracy score of 94.84%. The classification of the finger types also shows a high accuracy of 92.39%, indicating the reliability. As demonstrated by the low mean Squared Error score and the high Structural Similarity Index score, the reconstruction of fingerprints using autoencoder models significantly improves the image quality to address practical limitations in the acquisition of clear images. These findings contribute to the development of techniques for identifying gender in forensic science, and in biometric analysis during criminal investigations. Future directions include refining feature extraction and classification models for accurate gender classification across diverse demographics, such as individuals from various countries and regions. Additionally, advancing fingerprint reconstruction techniques aims to overcome practical limitations in forensic image acquisition, enhancing overall gender classification accuracy in forensic science and biometric analysis.
关键词
Human gender classification,Deep learning,Fingerprint Reconstruction,Autoencoder,Forensic image acquisition
报告人
Umesh Eranda Ranasooriya
Lecturer (Probationa Uva Wellassa University稿件作者
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重要日期
-
会议日期
10月24日
2024
至10月27日
2024
-
10月14日 2024
初稿截稿日期
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10月29日 2024
注册截止日期
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10月31日 2024
报告提交截止日期
主办单位
国际科学联合会
IEEE泰国分会
IEEE计算机学会泰国分会
IEEE泰国分会
IEEE计算机学会泰国分会
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2025年12月19日 约旦 Zarqa
2025亚洲通信与网络会议
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