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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.10, No.8, Aug. 2018

Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

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Author(s)

Ranganatha S, Y P Gowramma

Index Terms

Tracking human face(s);Different background;Video sequences;FAST features;Corner points;Low resolution

Abstract

We are proposing a novel algorithm for tracking human face(s) in different background video sequences. We have trained both face and non-face images which help in face(s) detection process. At first, FAST features and corner points are extracted from the detected face(s). Further, mid points are calculated from corner points. FAST features, corner points and mid points are combined together. Using the combined points, point tracker tracks face(s) in the frames of the video sequence. Standard metrics were adopted for measuring the performance of the proposed algorithm. Low resolution video sequences with challenges such as partial occlusion, changes in expression, variations in illumination and pose took part while testing the proposed algorithm. Test results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.

Cite This Paper

Ranganatha S, 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, 2018.DOI: 10.5815/ijigsp.2018.08.05

Reference

[1]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), in press.

[2]Alireza Tofighi, Nima Khairdoost, S. Amirhassan Monadjemi, and Kamal Jamshidi, “A Robust Face Recognition System in Image and Video”, International Journal of Image, Graphics and Signal Processing (IJIGSP), vol.6, no.8, pp.1-11, July 2014. DOI: 10.5815/ijigsp.2014.08.01

[3]E. Rosten and T. Drummond, “Fusing Points and Lines for High Performance Tracking”, in Proc. of IEEE International Conference on Computer Vision (ICCV), vol.2, pp.1508-1515, October 2005. DOI: 10.1109/ICCV.2005.104

[4]C. Harris and M. Stephens, “A Combined Corner and Edge Detector”, in Proc. of 4th Alvey Vision Conference, Manchester, UK, pp.147-151, 1988.

[5]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), Kauai, USA, vol.1, pp.511-518,  December 2001. DOI: 10.1109/CVPR.2001.990517

[6]P. Viola and M. Jones, “Robust Real-Time Face Detection”, International Journal of Computer Vision (IJCV), vol.57, pp.137-154, 2004.

[7]http://www.mathworks.com/help/vision/ug/train-a-cascade-object-detector.html.

[8]Elena Alionte and Corneliu Lazar, “A Practical Implementation of Face Detection by Using Matlab Cascade Object Detector”, in Proc. of IEEE International Conference on System Theory, Control and Computing (ICSTCC), pp.785-790, October 2015. DOI: 10.1109/ICSTCC.2015.7321390

[9]Deepa and K Jyothi, “A Robust and Efficient Pre Processing Techniques for Stereo Images”, in Proc. of IEEE International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp.89-92, December 2017. DOI: 10.1109/ICEECCOT.2017.8284645

[10]Mahesh and Subramanyam M. V, “Feature Based Image Mosaic Using Steerable Filters and Harris Corner Detector”, International Journal of Image, Graphics and Signal Processing (IJIGSP), vol.5, no.6, pp.9-15, May 2013. DOI: 10.5815/ijigsp.2013.06.02

[11]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.

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

[13]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. DOI: 10.1109/CVPR.1994.323794

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

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

[16]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

[17]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

[18]J. Strom, T. Jebara, S. Basu, and A. Pentland, “Real Time Tracking and Modeling of Faces: An EKF-based Analysis by Synthesis Approach”, in Proc. of IEEE International Workshop on Modelling People (MPeople), pp.55-61, September 1999. DOI: 10.1109/PEOPLE.1999.798346

[19]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.

[20]Dr. Ravi Kumar Jatoh, Sanjana Gopisetty, and 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  

[21]Stefan Leutenegger, Margarita Chli, and Roland Y. Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints”, in Proc. of IEEE International Conference on Computer Vision (ICCV), pp.2548-2555, November 2011. DOI: 10.1109/ICCV.2011.6126542

[22]Anelia Angelova, Yaser Abu-Mostafa, and Pietro Perona, “Pruning Training Sets for Learning of Object Categories”, in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol.1, pp.494-501, June 2005. DOI: 10.1109/CVPR.2005.283

[23]D. Hond and L. Spacek, “Distinctive Descriptions for Face Processing”, in Proc. of 8th British Machine Vision Conference (BMVC), Colchester, England, vol.1, pp.320-329, September 1997.

[24]F. S. Samaria and A. C. Harter, “Parameterisation of a Stochastic Model for Human Face Identification”, in Proc. of IEEE Workshop on Applications of Computer Vision, Sarasota, USA, pp.138-142, December 1994. DOI: 10.1109/ACV.1994.341300

[25]“Face Database from Robotics Lab of National Cheng Kung University, Taiwan”, URL: http://robotics.csie.ncku.edu.tw/Databases/FaceDetect_PoseEstimate.htm.

[26]Kuang-Chih Lee, J. Ho, and D. J. Kriegman, “Acquiring Linear Subspaces for Face Recognition Under Variable Lighting”, in IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol.27, no.5, pp.684-698, March 2005. DOI: 10.1109/TPAMI.2005.92

[27]A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose”, in IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol.23, no.6, pp.643-660, June 2001. DOI: 10.1109/34.927464

[28]Li Fei-Fei, R. Fergus, and P. Perona, “Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories”, in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), July 2004. DOI: 10.1109/CVPR.2004.383

[29]Li Fei-Fei, R. Fergus, and P. Perona, “One-Shot Learning of Object Categories”, in IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol.28, no.4, pp.594-611, April 2006. DOI: 10.1109/TPAMI.2006.79

[30]G. Griffin, A. Holub, and P.Perona, “Caltech-256 Object Category Dataset”, Technical Report 7694, California Institute of Technology, pp.1-20, 2007. URL: http://authors.library.caltech.edu/7694

[31]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), pp.1-8, June 2008. DOI: 10.1109/CVPR.2008.4587572

[32]C. Sanderson and B.C. Lovell, “Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference”, in Proc. of International Conference on Biometrics, Lecture Notes in Computer Science (LNCS), vol.5558, pp.199-208, 2009.

[33]https://in.mathworks.com/downloads/R2018a/toolbox/vision/visiondata. 

[34]D. M. W. Powers, “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation”, Journal of Machine Learning Technologies (JMLT), vol.2, no.1, pp.37-63, 2011.

[35]T. Fawcett, “An Introduction to ROC Analysis”, Pattern Recognition Letters, vol.27, no.8, pp.861-874, June 2006. DOI: 10.1016/j.patrec.2005.10.010

[36]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.