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Analyzing different Techniques for Face Detection and Recognition

by Sahil Bisht, Sonal Fatangare, Apeksha Patil, Aakanksha Panadi, Dev Kulkarni
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 16
Year of Publication: 2024
Authors: Sahil Bisht, Sonal Fatangare, Apeksha Patil, Aakanksha Panadi, Dev Kulkarni
10.5120/ijca2024923550

Sahil Bisht, Sonal Fatangare, Apeksha Patil, Aakanksha Panadi, Dev Kulkarni . Analyzing different Techniques for Face Detection and Recognition. International Journal of Computer Applications. 186, 16 ( Apr 2024), 39-47. DOI=10.5120/ijca2024923550

@article{ 10.5120/ijca2024923550,
author = { Sahil Bisht, Sonal Fatangare, Apeksha Patil, Aakanksha Panadi, Dev Kulkarni },
title = { Analyzing different Techniques for Face Detection and Recognition },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2024 },
volume = { 186 },
number = { 16 },
month = { Apr },
year = { 2024 },
issn = { 0975-8887 },
pages = { 39-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number16/analyzing-different-techniques-for-face-detection-and-recognition/ },
doi = { 10.5120/ijca2024923550 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-04-27T03:06:46.110331+05:30
%A Sahil Bisht
%A Sonal Fatangare
%A Apeksha Patil
%A Aakanksha Panadi
%A Dev Kulkarni
%T Analyzing different Techniques for Face Detection and Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 16
%P 39-47
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face detection and recognition are two critical tasks in the realm of computer vision with a diverse array of real-world applications, encompassing surveillance, security, human-computer interaction, and biometric authentication systems. This survey paper offers an exhaustive review of the advancements in face detection and recognition techniques over the past years. The paper first delves into the fundamental concepts and challenges associated with face detection, like changing lighting scenarios, obstructed views, and pose fluctuations. Various traditional and deep learning face detection algorithms are then analyzed, highlighting their strengths and limitations. Subsequently, this study explores the intricacies of face recognition, emphasizing the significance of feature extraction, representation, and matching methods. It discusses the evolution from classical methods, such as eigenfaces and Fisherfaces, to cutting-edge deep learning techniques, such as advanced convolutional neural networks. Further this paper investigates the issues related to facial expression variations, aging, and demographic biases in face recognition systems. Finally, the survey concludes with a comprehensive comparative analysis of various benchmark datasets, evaluation metrics, and performance measures used in assessing the efficacy of algorithms. It also highlights the future research directions and emerging trends in the field, including multimodal fusion, cross-modal face recognition, and the integration of deep learning with generative models for robust and efficient face analysis.

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Index Terms

Computer Science
Information Sciences

Keywords

Face Recognition Face Detection Convolutional Neural Network Deep Learning Industrial Application

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