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An Investigation on Face Detection Applications

by Mohammad Almasi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 21
Year of Publication: 2019
Authors: Mohammad Almasi
10.5120/ijca2019919664

Mohammad Almasi . An Investigation on Face Detection Applications. International Journal of Computer Applications. 177, 21 ( Dec 2019), 17-23. DOI=10.5120/ijca2019919664

@article{ 10.5120/ijca2019919664,
author = { Mohammad Almasi },
title = { An Investigation on Face Detection Applications },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2019 },
volume = { 177 },
number = { 21 },
month = { Dec },
year = { 2019 },
issn = { 0975-8887 },
pages = { 17-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number21/31020-2019919664/ },
doi = { 10.5120/ijca2019919664 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:46:29.969946+05:30
%A Mohammad Almasi
%T An Investigation on Face Detection Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 21
%P 17-23
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face detection in uncontrolled ambiance continues to be a challenge to traditional face detection methods due to the large difference in facial expressions. The task of alignment is particularly difficult when the face comes from an extremely unconditional environment. To overcome these problems, the present paper is a reliable and deeply considerable application that allows users to detect a face(s) in real-time and process them. Database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. Then methods for morphing, warping, swapping and averaging of faces are presented. As a result, faces can be very effectively detected, aligned/ oriented and used for the above methods. In addition, based on results this approach can detect faces and eyes in difficult conditions without explicitly simulating their variation. Evaluating the tests of the application, it can confidently said that this face detection method is accurate and effective and reaches the most modern level of performance. The same methodology can easily be generalized to other tasks, as well as the detection of a common object.

References
  1. A. Pakhirka, Thesis for a scientific degree: Computer method of face localization in images in difficult lighting conditions; p.p. 8-10, 2011.
  2. A.K. Jain, R. Bolle, and S. Pankanti, eds., Biometrics: Personal Identification in a Networked Society, Kluwer Academic Publishers, 1999.
  3. A Krenker, J Bešter and A Kos,” Introduction to the Artificial Neural Networks”, Edited Kenji Suzuki, Published by InTech,, Janeza Trdine, Croatia, p.p. 3-18, 2011.
  4. H. A. Rowley, S. Baluja and T. Kanade, “Neural Network-based Face detection”, IEEE Trans. On Pattern Recognition and Machine Intelligence, 20(1),23-28, 1998.
  5. W.Zhao, R.Chellapa, P.J.Phillips, and A.Rosenfeld, “Face Recognition: A Literature Survey,” Technical Report CART-TR948. University of Maryland, Aug.2002.
  6. W. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips. Face recognition: A literature survey. ACM Computing Surveys; p.p. 399–458, 2003.
  7. Er.P. Kumar, Er.P. Sharma, “ARTIFICIAL NEURAL NETWORKS- A Study”, International Journal of Emerging Engineering Research and Technology, vol. 2, no. 2, p.p. 143-148, 2014.
  8. N Jindal, V Kumar,” Enhanced Face Recognition Algorithm using PCA with Artificial Neural Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, vol 3, no. 6, p.p. 864-872, 2013.
  9. S. Ren, X. Cao, Y. Wei, and J. Sun. Face alignment at 3000 fps via regressing local binary features. In CVPR, 2014
  10. L. Breiman. Random forests. Machine learning, 45(1): p.p. 5–32, 2001.
  11. X. Cao, Y. Wei, F. Wen, and J. Sun. Face alignment by explicit shape regression. International Journal of Computer Vision, 107(2): p.p. 177–190, 2014.
  12. Caruana, R.: Multitask learning. Machine learning 28(1), p.p. 41–75, 1997.
  13. Z. Zhang, P. Luo, C. C. Loy, and X. Tang. Facial landmark detection by deep multi-task learning. In ECCV, p.p. 94– 108, 2014.
  14. J. Zhang, S. Shan, M. Kan, and X. Chen. Coarse-to-fine autoencoder networks (CFAN) for realtime face alignment. In ECCV, p.p. 1–16, 2014.
  15. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: CVPR: p.p. 3476–3483, 2013.
  16. X. Zhu, D. Ramanan. "Face detection, pose estimation and landmark localization in the wild" Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island, June 2012.
  17. Z. Zhang. “Facial Landmark Detection by Deep Multi-task Learning”. In: Computer Vision – ECCV 2014 Lecture Notes in Computer Science, p.p. 94– 108, 2014.
  18. J. Halberstadt, G. Rhodes. “The Attractiveness of Non-face Averages: Implications for an Evolutionary Explanation of the Attractiveness of Average Faces”, July 2000.
  19. J. Halberstadt, G. Rhodes. “It’s not just average faces that are attractive: Computer-manipulated averageness makes birds, fish, and automobiles attractive”, 2003
  20. J.H. Koeslag. “Koinophilia: Groups sexual creatures into species, promotes stasis, and stabilizes social behaviour, Journal of Theoretical Biology”, Vol.144, No.1, p.p.15–35, 1990.
  21. P. Benson, D. Perrett. ‘Computer averaging and manipulations of faces’. In: Photovideo: Photography in the age of the computer, p.p. 32–38, 1991.
  22. F. Galton. ‘Composite portraits’. Journal of the Anthropological Institute of Great Britain and Ireland 8, p.p. 132–142, 1878.
  23. P. Perez, M. Gangnet, A. Blake. “Poisson Image Editing”, Journal of ACM Transactions on Graphics (TOG), Vol. 22 - 3, p.p. 313-318, 2003.
  24. S. Mallick. Average Face: OpenCV (C++ / Python)Tutorial.url:http://www.learnopencv.com/average-face-opencv-c-python-tutorial, 2016.
  25. M. Turk. “A random walk through Eigen space. IEICE Transactions on Information and Systems”, E84-D(12): p.p. 1586–1595, December 2001.
  26. Average Face: OpenCv (Python, C++) Tutorial. url:https://www.learnopencv.com/average-face-opencv-c-python-tutorial
  27. Recognition of individual faces and average. URL: psych.mcmaster.ca/maurerlab/Publications/deHaanPrototype.pdf.
  28. The ‘Averageness Hypothesis’ of attractiveness. URL: http://faceresearch.org/students/averageness.
  29. Average Face – Creative AI. URL: www.creativeai.net/posts/Fods4e9m8nGiy3F5a.
  30. Face Alignment with OpenCV and Python. URL: www.creativeai.net/posts/Fods4e9m8nGiy3F5a.
  31. Face Alignment with OpenCV and Python. URL: www.creativeai.net/posts/Fods4e9m8nGiy3F5a.
  32. Harry Wechsler, Andeep S. Toor, Modern art challenges face detection, Pattern Recognition Letters (2018), doi: 10.1016/j.patrec.2018.02.014
  33. Hong Ai, Xuebin Cheng, Research on embedded access control security system and face recognition system, Measurement (2018),doi: 10.1016/j.measurement.2018.04.005.
  34. Hazar Mliki, Mohamed Hammami, Hanêne Ben-Abdallah, Real time face detection based on motion and skin color information, Parallel and Distributed Processing with Applications, (2012), 10th IEEE International Symposium on Parallel and Distributed Processing with Applications, doi: 10.1109/ISPA.2012.117.
  35. Fehmi Jaafar, Angela Lozano, Yann-Ga¬ el Gu«eh«eneuc, Kim Mens, Analyzing Software Evolution and Quality by Extracting Asynchrony Change patterns, The Journal of Systems & Software (2017), doi: 10.1016/j.jss.2017.05.047.
  36. Fotini Patrona, Anargyros Chatzitofis, Dimitrios Zarpalas, Petros Daras, motion Analysis: Action Detection, Recognition and Evaluation based on motion capture data, Pattern Recognition (2017), doi: 10.1016/j.patcog.2017.12.007.
  37. Li Cuimei, Qi Zhiliang, Jia Nan, Wu Jianhua, Human face detection algorithm via Haar cascade classifier combined with three additional classifiers, Electronic Measurement & Instruments (ICEMI), 2017 13th IEEE International Conference, doi: 10.1109/ICEMI.2017.8265863.
  38. Dhananjay Rathod, Vinay A, Shylaja SS and SNatarajan, Facial Landmark Localization -A Literature Survey, International Journal of Current Engineering and Technology(2014), inpressco.com/category/ijcet.
  39. Margaret Collins, The Attractiveness of the Average Face, Seminars in Orthodontics, Volume 18, Issue3, September 2012, Pages 217-228, doi: 10.1053/j.sodo.2012.04.007.
  40. He, Xiaofe; Yan, Shuicheng; Niyogi, Partha and Zhang, HongJiang (2005). Face Recognition Using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3): 328-340.
  41. D.R.Kasat, Sanjeev Jain, V. M. Thakare, A Survey of Face Morphing Techniques, nternational Journal of Computer Applications, Second National Conference on Recent Trends in Information Security, GHRCE, Nagpur, India, Jan-2014.
Index Terms

Computer Science
Information Sciences

Keywords

Face detection Facial landmark detection and recognition

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