Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences

Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences

Автор: Ranganatha S., Y. P. Gowramma

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 4 vol.14, 2022 года.

Бесплатный доступ

We are proposing a unique novel algorithm for tracking human face(s) in different background video sequences. In the beginning, Eigen features and corner points are extracted from the detected face(s). HOG (Histograms of Oriented Gradients) features are isolated from corner points. Eigen and HOG features are combined together. Using these combined features, point tracker keeps track of the face(s) in the frames of the video sequence. Proposed algorithm is being tested on challenging datasets video sequences with technical challenges such as partial occlusion (e.g. moustache, beard, spectacles, helmet, headscarf etc.), changes in expression, variations in illumination and pose; and measured for performance using standard metrics such as accuracy, precision, recall and specificity. Experimental results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.

Еще

Tracking human face(s), Different background, Video sequences, Eigen features, Corner points, HOG features, Point tracker, Challenging datasets, and Standard metrics

Короткий адрес: https://sciup.org/15018500

IDR: 15018500   |   DOI: 10.5815/ijigsp.2022.04.06

Список литературы Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences

  • Ranganatha S and Dr. Y P Gowramma, “Face Recognition Techniques: A Survey”, International Journal for Research in Applied Science and Engineering Technology (IJRASET), ISSN: 2321-9653, Vol.3, No.4, pp.630-635, April 2015.
  • P. Viola and M. Jones, “Robust Real-Time Face Detection”, International Journal of Computer Vision (IJCV), Vol.57, pp.137-154, 2004.
  • P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features”, in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, Vol.1, pp.511-518, 2001.
  • Kah-Kay Sung and Tomasi Poggio, “Example-Based Learning for View-Based Human Face Detection”, in IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Vol.20, No.1, pp.39-51, January 1998.
  • H. Rowley, S. Baluja and T. Kanade, “Neural Network-Based Face Detection”, in IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Vol.20, No.1, pp.22-38, January 1998.
  • D. Roth, M. Yang and N. Ahuja, “A Snow Based Face Detector”, Advances of Neural Information Processing Systems, MIT Press, pp.855-861, 2000.
  • Yasunori Kudo and Yoshimitsu Aoki, “Dilated Convolutions for Image Classification and Object Localization”, in Proc. of International Conference on Machine Vision Applications (MVA), Nagoya University, Nagoya, Japan, pp.452-455, May 2017.
  • Jianbo Shi and Carlo Tomasi, “Good Features to Track”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.593-600, June 1994.
  • Maria Rosario D. Rodavia, Orlando Bernaldez and Maylane Ballita, “Web and Mobile Based Facial Recognition Security System Using Eigenfaces Algorithm”, in Proc. of IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp.86-92, December 2016.
  • C. Harris and M. Stephens, “A Combined Corner and Edge Detector”, in Proc. of 4th Alvey Vision Conference, Manchester, UK, pp.147-151, 1988.
  • R. Manoranjitham and P. Deepa, “Novel Interest Point Detector Using Bilateral-Harris Corner Method”, in Proc. of IEEE International Conference on Advanced Computing and Communication Systems (ICACCS), pp.1-4, January 2017.
  • N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection”, in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Vol.1, pp.886-893, June 2005.
  • David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision (IJCV), Vol.60, No.2, pp.91-110, November 2004.
  • Sidheswar Routray, Arun Kumar Ray and Chandrabhanu Mishra, “Analysis of Various Image Feature Extraction Methods against Noisy Image: SIFT, SURF and HOG”, in Proc. of IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp.1-5, February 2017.
  • Herbert Bay, Andreas Ess, Tinne Tuytelaars and Luc Van Gool, “Speeded-Up Robust Features”, Computer Vision and Image Understanding (CVIU), Vol.110, No.3, pp.346-359, June 2008.
  • Bruce D. Lucas and Takeo Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision”, in Proc. of International Joint Conference on Artificial Intelligence, 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.
  • Ranganatha S and Y P Gowramma, “A Novel Fused Algorithm for Human Face Tracking in Video Sequences”, in Proc. 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, “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
  • G. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface”, Intel Technology Journal, pp.12-21, 1998.
  • Douglas Decarlo and Dimitris N.Metaxas, “Optical Flow Constraints on Deformable Models with Applications to Face Tracking”, International Journal of Computer Vision (IJCV), Vol.38, No.2, pp.99-127, July 2000.
  • M. Kim, S. Kumar, V. Pavlovic and H. Rowley, “Face Tracking and Recognition with Visual Constraints in Real-World Videos”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2008.
  • 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
  • Yongkang Wong, Shaokang Chen, Sandra Mau, Conrad Sanderson and Brian C. Lovell, “Patch-Based Probabilistic Image Quality Assessment for Face Selection and Improved Video-Based Face Recognition”, in proc. of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.74-81, June 2011.
  • Ivan Laptev, Marcin Marszalek, Cordelia Schmid and Benjamin Rozenfeld, “Learning Realistic Human Actions from Movies”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, June 2008.
  • C. Sanderson and B.C. Lovell, “Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference”, Lecture Notes in Computer Science (LNCS), Vol.5558, pp.199-208, 2009.
  • T. Fawcett, “An Introduction to ROC Analysis”, Pattern Recognition Letters, Vol.27, No.8, pp.861-874, 2006.
  • Ranganatha S and 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
  • 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.
  • Ranganatha S, 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, 2018. DOI: 10.5815/ijitcs.2018.11.04.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp