A Pragamatic Approach for Real Time Face Tracking

Authors

  • Ranganatha S. Department of Computer Science and Engineering, Government Engineering College, Kushalnagar, Karnataka, India
  • Thirthe Gowda M. T. Department of Computer Science and Engineering, Government Engineering College, Hassan, Karnataka, India
  • Shivashankara S. Department of Computer Science and Engineering, Government Engineering College, KR.Pet, Mandya, Karnataka, India
  • Ravi P. Department of Computer Science and Engineering, Mysore College of Engineering and Management, Mysore, Karnataka, India

Keywords:

Live Detection and Tracking, Video Sequences, Technical Challenges, Video Analysis, Algorithm

Abstract

Live detection and tracking of face(s) is a challenging task in the field of video processing. This paper proposes the detection and live tracking of a single or multiple faces using live video sequences from a webcam with few technical challenges such as partial occlusion, different light intensity levels, and when camera faces different angles.  In today's reality, video analysis is an important task that can help in difficult times. Real time face detection and tracking is one such part of video analysis which is tremendously in use. Today’s productive cameras and high efficiency computers have made the researchers to develop better algorithms for processing the video sequences. Every now and then, system vision and the things we can achieve using this is spoken for versatility in the field of computer vision. Proposed algorithm detect and track the faces very efficiently even when there are constraints from external factors.

Downloads

Download data is not yet available.

References

Ranganatha S, Dr. Y P Gowramma, “Face Recognition Techniques: A Survey”, International Journal for Research in Applied Science and Engineering Technology, Vol.3, Issue 4, pp.630-635, 2015.

Ranganatha S, Y P Gowramma, “A Comprehensive Survey of Algorithms for Face Tracking in Different Background Video Sequence”, International Journal of Computer Applications, Vol.181, Issue 27, pp.43-49, 2018. DOI: 10.5120/ijca2018918134

P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features”, in Procedings. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, USA, Vol.1, pp.511-518, December 2001. DOI: 10.1109/CVPR.2001.990517

Jatin Chatrath, Pankaj Gupta, Puneet Ahuja, Aryan Goel and Shaifali M. Arora, “Real Time Human Face Detection and Tracking”, in Procedings of IEEE International Conference on Signal Processing and Integrated Networks, Noida, India, pp.705-710, February 2014. DOI: 10.1109/SPIN.2014.6777046

D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis”, in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol.24, No.5, pp.603-619, May 2002. DOI: 10.1109/34.1000236

K. Fukunaga and L. D. Hostetler, “The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition”, in IEEE Transations on Information Theory, Vol.21, No.1, pp.32-40, January 1975. DOI: 10.1109/TIT.1975.1055330

G. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface”, Intel Technology Journal, pp.12-21, 1998.

Ranganatha S and Y P Gowramma, “An Integrated Robust Approach for Fast Face Tracking in Noisy Real-World Videos with Visual Constraints”, in Proc. of IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.772-776, September 2017. DOI: 10.1109/ICACCI.2017.8125935

Ranganatha S and Y P Gowramma, “Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence”, International Journal of Information Technology and Computer Science (IJITCS), Vol.10, No.11, pp.39-48, November 2018. DOI: 10.5815/ijitcs.2018.11.04

Han-Pang Huang and Chun-Ting Lin, “Multi-CAMSHIFT for Multi-View Faces Tracking and Recognition”, in Procedings of IEEE International Conference on Robotics and Biomimetics, Kunming, China, pp.1334-1339, December 2006. DOI: 10.1109/ROBIO.2006.340122

N. Al-Najdawi, S. Tedmori, E. A. Edirisinghe and H. E. Bez, “An Automated Real-Time People Tracking System Based on KLT Features Detection”, International Arab Journal of Information Technology, Vol.9, No.1, pp.100-107, 2012.

Bruce D. Lucas and Takeo Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision”, in Procedings of International Joint Conference on Artificial Intelligence, Vancouver, BC, Canada, Vol.2, pp.674-679, August 1981.

Carlo Tomasi and Takeo Kanade, “Detection and Tracking of Point Features”, Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.

J. Savitha, Dr. A.V. Senthil Kumar, “Face Tracking and Detection using S-PCA & KLT Method”, International Journal of Advance Research in Computer Science and Management Studies”, Vol.2, No.2, pp.224-229, February 2014. ISSN: 2321-7782

Jianbo Shi and Carlo Tomasi, “Good Features to Track”, in Procedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.593-600, June 1994. DOI: 10.1109/CVPR.1994.323794

Ranganatha S and Y P Gowramma, “A Novel Fused Algorithm for Human Face Tracking in Video Sequences”, in Procedings of IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), pp.1-6, October 2016. DOI: 10.1109/CSITSS.2016.7779430

Ranganatha S and Y P Gowramma, “Development of Robust Multiple Face Tracking Algorithm and Novel Performance Evaluation Metrics for Different Background Video Sequences”, International Journal of Intelligent Systems and Applications (IJISA), Vol.10, No.8, pp.19-35, August 2018. DOI: 10.5815/ijisa.2018.08.03

Ranganatha S and Y P Gowramma, “Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.14, No.4, pp. 70-83, 2022. DOI: 10.5815/ijigsp.2022.04.06

Ranganatha S and Y P Gowramma, “Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.10, No.8, pp.39-53, August 2018. DOI: 10.5815/ijigsp.2018.08.05

Ranganatha S and Y P Gowramma, “Image Training and LBPH Based Algorithm for Face Tracking in Different Background Video Sequence”, International Journal of Computer Sciences and Engineering (IJCSE), Vol.6, No.9, pp.349-354, September 2018. CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i9.349354

Ranganatha S and Y P Gowramma, “Selected Single Face Tracking in Technically Challenging Different Background Video Sequences Using Combined Features”, ICTACT Journal on Image and Video Processing (JIVP), Vol.9, No.2, pp.1911-1918, November 2018. DOI: 10.21917/ijivp.2018.0271

Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic and Henry Rowley, “Face Tracking and Recognition with Visual Constraints in Real-World Videos”, in Procedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, June 2008. DOI: 10.1109/CVPR.2008.4587572

Chun-Ho Cheung and Lai-Man Po, “A Novel Cross-Diamond Search Algorithm for Fast Block Motion Estimation”, in IEEE Transactions. on Circuits and Systems for Video Technology, Vol.12, No.12, pp.1168-1177, December 2002. DOI: 10.1109/TCSVT.2002.806815.

Maciej Smiatacz, “Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces - How to Face the Face Verification Task”, In: Burduk R., Jackowski K., Kurzynski M., Wozniak M., Zolnierek A. (eds) Proc. of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, Vol. 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_18

Delpiah Wahyuningsih, Chandra Kirana, Rahmat Sulaiman, Hamidah, Triwanto, “Comparison of the Performance of Eigenface and Fisherface Algorithm in the Face Recognition Process”, in Proc. of IEEE International Conference on Cyber and IT Service Management, pp.1-5, November 2019. DOI: 10.1109/CITSM47753.2019.8965345

Ravi Kumar Jatoth, Sanjana Gopisetty, Moiz Hussain, “Performance Analysis of Alpha Beta Filter, Kalman Filter and Meanshift for Object Tracking in Video Sequences”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.7, No.3, pp.24-30, February 2015. DOI: 10.5815/ijigsp.2015.03.04

Downloads

Published

27.12.2023

How to Cite

S., R. ., Gowda M. T., T. ., S., S., & P., R. . (2023). A Pragamatic Approach for Real Time Face Tracking. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 205–214. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4266

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.