Enhancing Face Recognition Accuracy Using the ED-FFP Extraction Method and Ensemble Learning for Forensics and Cyber Security

Pranav Virmani, Srikanth Prabhu, Ramya S*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Currently, Face Recognition is the most used biometric to determine an individual’s identity due to its natural and unobtrusive nature. This study proposes face recognition and verification of two-dimensional(2D) images by a feature extraction algorithm which involves identifying anthropological facial feature points and calculating the Euclidean Distance (ED) between these points (ED-FFP) as these distances, if required can be used as an objective measure during trials in courts. These measurements are then used as inputs for various classification methods, including Logistic Regression Classifier (LR), Decision Tree (DT), Naive Bayes (NB), and two other classifiers using the Ensemble Learning Model. The method was tested on 2D face image databases (Caltech, Yale, and ORL) and found to be more efficient and accurate for face recognition than other methods, with a maximum accuracy of 85% for predicting distinct faces using the Decision Tree classifier model. The ensemble learning model also had an accuracy of 85%, which could potentially be improved by using more photos for comparison. In future work, the method could be applied to 3D images, which is currently an open challenge in the field.

Original languageEnglish
Title of host publicationApplications and Techniques in Information Security - 13th International Conference, ATIS 2022, Revised Selected Papers
EditorsSrikanth Prabhu, Shiva Raj Pokhrel, Gang Li
PublisherSpringer Science and Business Media Deutschland GmbH
Pages130-142
Number of pages13
ISBN (Print)9789819922635
DOIs
Publication statusPublished - 2023
Event13th International Conference on Applications and Techniques in Information Security, ATIS 2022 - Manipal, India
Duration: 30-12-202231-12-2022

Publication series

NameCommunications in Computer and Information Science
Volume1804 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference13th International Conference on Applications and Techniques in Information Security, ATIS 2022
Country/TerritoryIndia
CityManipal
Period30-12-2231-12-22

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics

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