research-article

The Statistical Learning Methods In image processing and Facial Recognition

Authors:
Tareq Abed Mohammed

Imam Ja'afar Al-Sadiq University, Iraq

Imam Ja'afar Al-Sadiq University, Iraq
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,
Sarbaz Omar Rafeeq

Altinbas University, Turkey

Altinbas University, Turkey
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,
Oguz Bayat

Altinbas University, Turkey

Altinbas University, Turkey
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ICEMIS'21: The 7th International Conference on Engineering & MIS 2021October 2021Article No.: 72Pages 1–12https://doi.org/10.1145/3492547.3492656

Published:14 December 2021Publication History

ICEMIS'21: The 7th International Conference on Engineering & MIS 2021

Pages 1–12

Editorial Notes

NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the ICEMIS 2021Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

ABSTRACT

The aim of this paper is to develop a new approach for The Statistical Learning Methods in image processing and Facial Recognition using the deep learning techniques in python. In the recent years there have been significant advances in face recognition by using deep neural networks. One of the potential next steps is to develop optimized 3D facial recognition. Shifting from 2D to 3D increases complexity of the problem by adding an- other dimension to data, making possible solutions more resource hungry. We will investigate different depth camera based facial recognition techniques and test their performance by deploying them on an embedded processor. We focus on applications for embedded systems and use a small low-resolution time of flight (ToF) camera with our system to keep overall system portable and compact. All faces images are then projected on the feature space (“face space”) to find the corresponding coordinators. The face space is composed of “Eigenfaces” or “Fisherfaces” which are actually eigenvectors found after doing a matrix composition - Eigen decomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. The algorithms were realized by Python in Anaconda. Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files.

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Index Terms

The Statistical Learning Methods In image processing and Facial Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
2. Software and its engineering

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Published in
ICEMIS'21: The 7th International Conference on Engineering & MIS 2021
October 2021
566 pages
ISBN:9781450390446
DOI:10.1145/3492547
Editors:
Askar Khikmetov
Rector of International Information Technology University, IITU, Kazakhstan
,
Yevgeniya Daineko
PhD, Professor, IITU, Kazakhstan
,
Madina Ipalakova
Associate professor, IITU, Kazakhstan
,
Shadi A. Aljawarneh
JUST, Jordan
,
Yevgeniya Daineko
IITU, Kazakhstan
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Facial Recognition
Linear Discrimination Analysis (LDA)
Low Computational Power
Machine Learning
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The Statistical Learning Methods In image processing and Facial Recognition

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