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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.

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.
  20. OpenCV. (2021). OpenCV Library Documentation. Retrieved from https://docs.opencv.org/4.7.0/index.html
  21. Kumar, S., & Gupta, P. (2015, November 17). Comparative Analysis of Intersection Algorithms on Queries using Precision, Recall and F-Score. International Journal of Computer Applications, 130(7), 28–36. https://doi.org/10.5120/ijca2015907042.
  22. Vujovic, E. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/ijacsa.2021.0120670.
  23. Herbert Bay. Surf: speed-up robust features. In 9th European Conference on Computer Vision, 2006, pages 404–417, 2006.
  24. Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. Lecture notes in computer sci ence, 3951:404–417, 2006.
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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