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Comparative Analysis of Face Detection Algorithms using Images

by Vandna Kumari, Barinderjit Kaur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 14
Year of Publication: 2023
Authors: Vandna Kumari, Barinderjit Kaur
10.5120/ijca2023922827

Vandna Kumari, Barinderjit Kaur . Comparative Analysis of Face Detection Algorithms using Images. International Journal of Computer Applications. 185, 14 ( Jun 2023), 30-40. DOI=10.5120/ijca2023922827

@article{ 10.5120/ijca2023922827,
author = { Vandna Kumari, Barinderjit Kaur },
title = { Comparative Analysis of Face Detection Algorithms using Images },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 14 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 30-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number14/32765-2023922827/ },
doi = { 10.5120/ijca2023922827 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:04.484191+05:30
%A Vandna Kumari
%A Barinderjit Kaur
%T Comparative Analysis of Face Detection Algorithms using Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 14
%P 30-40
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Facial recognition plays a vital role in computer vision applications. Several face detection algorithms have been developed over time to accurately detect human faces in images and videos. This research paper presents a comparative analysis of popular face detection algorithms, including Viola-Jones, Haar Cascade, LBP (local binary patterns), SIFT (scale-invariant feature transform), and ORB (Oriented FAST and Rotated BRIEF), using precision, recall, F1-score, accuracy, and execution time as evaluation metrics. The objective is to assess the performance of these algorithms in detecting faces accurately and efficiently. The experiments were conducted on a dataset of images, and the results indicate varying levels of performance across the algorithms. Viola-Jones and Haar cascade classifier algorithms offer high accuracy and fast performance but may struggle in low-light conditions and with complex backgrounds. LBP, SIFT, and ORB algorithms showed trade-offs between accuracy and execution time but performed well under challenging conditions, such as low light, obstacles, and non-face objects. The findings of this analysis can aid researchers and practitioners in selecting the most suitable face detection algorithm based on their specific requirements and constraints. Further research can focus on hybrid approaches that combine the strengths of these algorithms or explore the potential of deep learning-based methods for improved face detection accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Computer vision Haar cascade classifier Viola-Jones SIFT ORB Local Binary Pattern cascade classifier.

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