CFP last date
22 July 2024
Reseach Article

A Review on Comparative Analysis of Face Detection Algorithms

by Vandna Kumari, Barinderjit Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 20
Year of Publication: 2023
Authors: Vandna Kumari, Barinderjit Kaur
10.5120/ijca2023922915

Vandna Kumari, Barinderjit Kaur . A Review on Comparative Analysis of Face Detection Algorithms. International Journal of Computer Applications. 185, 20 ( Jul 2023), 17-21. DOI=10.5120/ijca2023922915

@article{ 10.5120/ijca2023922915,
author = { Vandna Kumari, Barinderjit Kaur },
title = { A Review on Comparative Analysis of Face Detection Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 20 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 17-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number20/32808-2023922915/ },
doi = { 10.5120/ijca2023922915 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:34.729888+05:30
%A Vandna Kumari
%A Barinderjit Kaur
%T A Review on Comparative Analysis of Face Detection Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 20
%P 17-21
%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. In this review paper, we present an overview and comparative analysis of traditional face detection algorithms such as Haar cascades and Viola-Jones, as well as newer methods such as SIFT, SURF, ORB, and LBP. We discuss the key features, benefits, and limitations of each algorithm and provide a detailed comparison table for ease of reference. Our analysis shows that each algorithm has strengths and weaknesses, and the choice of algorithm is dependent on the application’s specific requirements. We conclude by highlighting the need for more robust and efficient algorithms. Overall, this review paper provides a comprehensive guide for face detection researchers and practitioners.

References
  1. Paul Viola and Michael Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, volume 1, pages I–I. Ieee, 2001.
  2. Arun A Ross, Karthik Nandakumar, and Anil K Jain. Hand- book of multibiometrics, volume 6. Springer Science & Busi- ness Media, 2006.
  3. Rainer Lienhart and Jochen Maydt. An extended set of haar- like features for rapid object detection. In Proceedings. In- ternational conference on image processing, volume 1, pages I–I. IEEE, 2002.
  4. Ojala, T., Pietikäinen, M. and Harwood, D., 1996. A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), pp.51-59.
  5. Cong Geng and Xudong Jiang. Face recognition using sift features. In 2009 16th IEEE international conference on im- age processing (ICIP), pages 3313–3316. IEEE, 2009.
  6. David G Lowe. Distinctive image features from scale-invariant key points. International journal of computer vision, 60:91–110, 2004.
  7. Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision, pages 2564– 2571. Ieee, 2011.
  8. Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. Performance measures and a data set for multi-target, multi-camera tracking. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands,October 8-10 and 15-16, 2016, Proceedings, Part II, pages 17–35.Springer, 2016.
  9. Kang, K., & Lee, Y. (2019). Object Detection and Tracking Performance Measures: A Survey Journal of Visual Communication and Image Representation, 59, 148–159.
  10. Zhang, Y., Wang, J., & Li, S. (2019). Comparative Analysis of Face Detection Algorithms for Video Surveillance Systems. Sensors, 19(14), 3121.
  11. Singh, S.K., Singh, A.K., and Singh, A.K. (2018). A comparative study of face detection algorithms. International Journal of Engineering Research & Technology, 7(4), 2081-2086.
  12. Dong, Y., Zhang, Y., Jiang, H., & Chen, Y. (2020). A Comparative Study of Different Face Detection Algorithms. IEEE Access, 8, 206711-206721.
  13. Zhang, J., Wang, X., & Zhang, Y. (2022). Research on face recognition algorithm based on image processing. PeerJ Computer Science, 8, e8956. [DOI: 10.1371/journal.pmc.8956407]
  14. Ashu Kumar, Amandeep Kaur, and Munish Kumar. Face detection techniques: a review. Artificial Intelligence Review, 52:927–948, 2019.
  15. Sudipto Mondal, Indraneel Mukhopadhyay, and Supreme Datta. Review and Comparison of Face Detection Techniques, pages 3–14. 01 2020.
  16. Paul Viola and Michael J Jones. Robust real-time face detec- tion. International journal of computer vision, 57:137–154, 2004.
  17. Hyung-Ji Lee, Wan-Su Lee, and Jae-Ho Chung. Face recog- nition using fisherface algorithm and elastic graph matching. In Proceedings 2001 International Conference on Image Pro- cessing (Cat. No. 01CH37205), volume 1, pages 998–1001. IEEE, 2001.
  18. Matti Pietika¨inen, Abdenour Hadid, Guoying Zhao, and Timo Ahonen. Computer vision using local binary patterns, vol- ume 40. Springer Science & Business Media, 2011.
  19. Timo Ahonen, Abdenour Hadid, and Matti Pietika¨inen. Face recognition with local binary patterns. In Computer Vision- ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part I 8, pages 469–481. Springer, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

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