Video and video types are changing day by day; due to which, video processing is becoming complex time to time. There is a lack of particular algorithm for automatic detection and tracking of human faces in video, to overcome the challenges that are being faced nowadays. This paper describes a model for detection and tracking of human faces in different background video sequence using OpenCV platform. Both positive and negative image samples are trained and saved as xml file. With the help of trained samples, LBPH algorithm clarifies whether the video frame contain faces or not. Further, HOG descriptor is fed to SVM detector to compute the coefficients that are stored in the xml file. Based on this, face regions are tracked until the last frame is reached. We have tested our proposed algorithm on the videos of a technically challenging dataset. Standard metrics helped to judge the success of the proposed algorithm. Test results indicate the superiority of our proposed model, compared to other similar algorithms.

Detection, Tracking of human faces, Different background, Video sequence, OpenCV, LBPH, HOG, SVM

[1] 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.
[2] P. Viola, M. Jones, “Robust Real-Time Face Detection”, International Journal of Computer Vision, Vol.57, pp.137-154, 2004.
[3] P. Viola, M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features”, in the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, USA, pp.511-518, 2001.
[4] Md. Abdur Rahim, Md. Najmul Hossain, Tanzillah Wahid, Md. Shafiul Azam, “Face Recognition using Local Binary Patterns (LBP)”, Global Journal of Computer Science and Technology, Vol.13, Issue.4, pp.1-8, 2013.
[5] N. Dalal, B. Triggs, “Histograms of Oriented Gradients for Human Detection”, in the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.886-893, 2005.
[6] Shantala Giraddi, Jagadeesh Pujari, Shivanand Seeri, “Identifying Abnormalities in the Retinal Images using SVM Classifiers”, International Journal of Computer Applications, Vol.111, Issue.6, pp.5-8, 2015.
[7] G. Bradski, “The OpenCV Library”, Dr. Dobbs Journal of Software Tools, 2000.
[8] G. Bradski, A.Kaebler, “Learning OpenCV”, China: Southeast Univ. Press, 2009.
[9] K. Fukunaga, L. D. Hostetler, “The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition”, IEEE Transactions on Information Theory, Vol.21, Issue.1, pp.32-40, 1975.
[10] Yizong Cheng, “Mean Shift, Mode Seeking, and Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.17, Issue.8, pp.790-799, 1995.
[11] G. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface”, Intel Technology Journal, pp.12-21, 1998.
[12] Bruce D. Lucas, Takeo Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision”, in the Proceedings of the International Joint Conference on Artificial Intelligence, pp. 674-679, 1981.
[13] Carlo Tomasi, Takeo Kanade, “Detection and Tracking of Point Features”, Carnegie Mellon University Technical Report CMU-CS-91-132, 1991.
[14] Jianbo Shi, Carlo Tomasi, “Good Features to Track”, in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.593-600, 1994.
[15] Ranganatha S, Y P Gowramma, “A Novel Fused Algorithm for Human Face Tracking in Video Sequences”, in the Proceedings of the IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, pp.1-6, 2016.
[16] C. Harris, M. Stephens, “A Combined Corner and Edge Detector”, in the Proceedings of the 4th Alvey Vision Conference, Manchester, UK, pp.147-151, 1988.
[17] Ranganatha S, Y P Gowramma, “An Integrated Robust Approach for Fast Face Tracking in Noisy Real-World Videos with Visual Constraints”, in the Proceedings of the IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, pp.772-776, 2017.
[18] Douglas Decarlo, Dimitris N. Metaxas, “Optical Flow Constraints on Deformable Models with Applications to Face Tracking”, International Journal of Computer Vision, Vol.38, Issue.2, pp.99-127, 2000.
[19] M. Kim, S. Kumar, V. Pavlovic, H. Rowley, “Face Tracking and Recognition with Visual Constraints in Real-World Videos”, in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, 2008.
[20] Ranganatha S, 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, Vol.10, Issue.8, pp.19-35, 2018.
[21] Stefan Leutenegger, Margarita Chli, Roland Y. Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints”, in the Proceedings of the IEEE International Conference on Computer Vision, pp.2548-2555, 2011.
[22] Ranganatha S, Y P Gowramma, “Eigen and HOG Features Algorithm for Face Tracking in Different Background Challenging Video Sequences”, International Journal of Computing, in press.
[23] 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 Vol.10, Issue.8, pp.39-53, 2018.
[24] E. Rosten, T. Drummond, “Fusing Points and Lines for High Performance Tracking”, in the Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp.1508-1515, 2005.
[25] Yongkang Wong, Shaokang Chen, Sandra Mau, Conrad Sanderson, Brian C. Lovell, “Patch-Based Probabilistic Image Quality Assessment for Face Selection and Improved Video-Based Face Recognition”, in the proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.74-81, 2011.
[26] Ivan Laptev, Marcin Marszalek, Cordelia Schmid, Benjamin Rozenfeld, “Learning Realistic Human Actions from Movies”, in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, 2008.
[27] C. Sanderson, B.C. Lovell, “Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference,” in the Proceedings of the International Conference of Biometrics, Alghero, Italy, pp.199-208, 2009.
[28] Ranganatha S, Y P Gowramma, “Selected Single Face Tracking in Technically Challenging Different Background Video Sequences Using Combined Features”, ICTACT Journal on Image and Video Processing, in press.
[29] 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, in press.
[30] D. M. W. Powers, “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation”, Journal of Machine Learning Technologies, Vol.2, Issue.1, pp.37-63, 2011.
[31] T. Fawcett, “An Introduction to ROC Analysis”, Pattern Recognition Letters, Vol.27, Issue.8, pp.861-874, 2006.
[32] Keni Bernardin, Rainer Stiefelhagen, “Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics”, EURASIP Journal on Image and Video Processing, pp.1-10, 2008.
[33] Anelia Angelova, Yaser Abu-Mostafa, Pietro Perona, “Pruning Training Sets for Learning of Object Categories”, in the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.494-501, 2005.
[34] “Face Database from Robotics Lab of National Cheng Kung University, Taiwan”, URL: http://robotics.csie.ncku.edu.tw/Databases/FaceDetect_PoseEstimate.htm.
[35] Li Fei-Fei, R. Fergus, P. Perona, “Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories”, in the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2004.
[36] Li Fei-Fei, R. Fergus, P. Perona, “One-Shot Learning of Object Categories”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.28, Issue.4, pp.594-611, 2006.
[37] G. Griffin, A. Holub, P.Perona, “Caltech-256 Object Category Dataset”, Technical Report 7694, California Institute of Technology, pp.1-20, 2007.