Skip to main content

2024 | OriginalPaper | Buchkapitel

Implementing Face Recognition Using Deep Learning

verfasst von : Allam Fatima Zohra, Hamami Mitiche Latifa, Bousbia-Salah Hicham

Erschienen in: Advances in Emerging Information and Communication Technology

Verlag: Springer Nature Switzerland

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Artificial Intelligence (AI) is one of the most active fields in science and engineering. It is used in several applications related to computer networks and their security. One of the main goals of AI is to allow machines to work automatically, intelligently and with minimal human intervention. Machine Learning (ML) is a subset of AI that works great for a wide variety of problems. It is used to solve problems with the aim of producing better results than existing traditional techniques. However, it encounters limitations due to the increase in data. This motivated researcher to find an effective alternative called Deep Learning (DL) which gives more accurate results in the presence of massive amounts of data. In this article, we are interested in the field of AI, more specifically DL, to design a biometric system capable of guaranteeing Automatic Facial Recognition to detect and authenticate an individual in real time. Common facial authentication methods involve extracting facial features to compare them to images stored in a database to find a suitable match.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat X. Peng, N. Ratha, S. Pankanti, Learning face recognition from limited training data using deep neural networks, in 2016 23rd International Conference on Pattern Recognition (ICPR) Cancún Center, Cancún, México, December 2016 X. Peng, N. Ratha, S. Pankanti, Learning face recognition from limited training data using deep neural networks, in 2016 23rd International Conference on Pattern Recognition (ICPR) Cancún Center, Cancún, México, December 2016
2.
Zurück zum Zitat M. Kas, Development of handcrafted and Deep based methods for face and facial expression recognition, PhD thesis in Computer Science, University of Technology of Belfort-Montbéliard (preparation establishment) and Distributed Knowledge and Artificial Intelligence, July 2021 M. Kas, Development of handcrafted and Deep based methods for face and facial expression recognition, PhD thesis in Computer Science, University of Technology of Belfort-Montbéliard (preparation establishment) and Distributed Knowledge and Artificial Intelligence, July 2021
3.
Zurück zum Zitat R.H. Andersen, T. Solund, J. Hallam, Definition and initial case-based evaluation of hardware-independent robot skills for industrial robotic CO-workers, in 41st International Symposium on Robotics, Munich, Germany, 2014, pp. 1–7 R.H. Andersen, T. Solund, J. Hallam, Definition and initial case-based evaluation of hardware-independent robot skills for industrial robotic CO-workers, in 41st International Symposium on Robotics, Munich, Germany, 2014, pp. 1–7
4.
Zurück zum Zitat B. Goertzel, Artificial general intelligence: Concept, state of the art, and future prospects. J. Artif. Gen. Intell. 5(1), 1–46 (2014)CrossRef B. Goertzel, Artificial general intelligence: Concept, state of the art, and future prospects. J. Artif. Gen. Intell. 5(1), 1–46 (2014)CrossRef
5.
Zurück zum Zitat C. Hardy, Contribution to the development of Deep Learning in distributed systems, PhD Thesis in Mathematics and Information and Communication Sciences and Technologies, Specialty: Computer Science, University of Rennes I, April 2019 C. Hardy, Contribution to the development of Deep Learning in distributed systems, PhD Thesis in Mathematics and Information and Communication Sciences and Technologies, Specialty: Computer Science, University of Rennes I, April 2019
6.
Zurück zum Zitat O. Biran, C. Cotton, Explanation and justification in Machine Learning: A survey, in IJCAI-17 Workshop on Explainable Artificial Intelligence (XAI), (2017) O. Biran, C. Cotton, Explanation and justification in Machine Learning: A survey, in IJCAI-17 Workshop on Explainable Artificial Intelligence (XAI), (2017)
7.
Zurück zum Zitat K. Crowston, F. Bolici, Impact of Machine Learning on work, in Proceedings of the 52nd Hawaii International Conference on System Sciences, (2019) K. Crowston, F. Bolici, Impact of Machine Learning on work, in Proceedings of the 52nd Hawaii International Conference on System Sciences, (2019)
8.
Zurück zum Zitat L. Deng, D. Yu, Deep Learning: Methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014)CrossRef L. Deng, D. Yu, Deep Learning: Methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014)CrossRef
9.
Zurück zum Zitat W. Hao, L. Qi, L. Xiaodong, A review of Deep Learning approaches for image classification and object segmentation. Comput. Mater. Contin. 60(2), 575–597 (2019) W. Hao, L. Qi, L. Xiaodong, A review of Deep Learning approaches for image classification and object segmentation. Comput. Mater. Contin. 60(2), 575–597 (2019)
10.
Zurück zum Zitat T. Guillod, P. Papamanolis, J.W. Kolar, Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design (Power Electronic Systems Laboratory, ETH Zurich, July 2020) T. Guillod, P. Papamanolis, J.W. Kolar, Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design (Power Electronic Systems Laboratory, ETH Zurich, July 2020)
11.
Zurück zum Zitat B. Zhang, L. Zhang, The analysis and improvement of Artificial Neural Network models, in 1997 IEEE International Conference on Intelligent Processing Systems, October 1997 B. Zhang, L. Zhang, The analysis and improvement of Artificial Neural Network models, in 1997 IEEE International Conference on Intelligent Processing Systems, October 1997
12.
Zurück zum Zitat M. Almseidin, M. Alzubi, S. Kovacs, Evaluation of Machine Learning algorithms for an intrusion detection system, in 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), October 2017 M. Almseidin, M. Alzubi, S. Kovacs, Evaluation of Machine Learning algorithms for an intrusion detection system, in 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), October 2017
Metadaten
Titel
Implementing Face Recognition Using Deep Learning
verfasst von
Allam Fatima Zohra
Hamami Mitiche Latifa
Bousbia-Salah Hicham
Copyright-Jahr
2024
DOI
https://doi.org/10.1007/978-3-031-53237-5_23

Premium Partner