Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification

Gamal Fahmy; Omar Fahmy; Mamdouh Fahmy

doi:doi:10.11648/j.ijics.20210603.14

| Peer-Reviewed

Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification

Gamal Fahmy, Omar Fahmy, Mamdouh Fahmy

Published in International Journal of Information and Communication Sciences (Volume 6, Issue 3)

Received: 8 September 2021 Accepted: 23 September 2021 Published: 30 September 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame sequence, and utilize this in biometric identification. We present a fast 2D Dual Discrete Wavelet Transform 2D-DWT based video magnification technique that detects micro movements by magnifying the phase differences between subsequent video frame's wavelet coefficients, at different sub bands. Next, in order to overcome shortcoming of 2D-DWT systems, 2D Dual Complex Wavelet Transform 2D-CWT has also been employed to estimate phase changes between subsequent video frames at different spatial locations of Complex Wavelets sub-bands. This latter presented CWT Technique uses the Radon Transform to detect any periodic motion in the video frames. Several simulation results are given to show that our proposed hybrid technique achieves comparable and sometimes superior performance with far less complexity when compared with recent literature in micro motion magnification, such as steerable pyramids STR and Riesz Transform RT based steerable pyramids RT-STR. Both DWT and CWT techniques are combined for 3D facial video identification. The attached videos demonstrate the superior video quality obtained by the proposed technique.

Published in	International Journal of Information and Communication Sciences (Volume 6, Issue 3)
DOI	10.11648/j.ijics.20210603.14
Page(s)	75-84
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Complex Wavelets, Micro Video Magnification, Local Phase Amplification

References

[1]	Jue Wang, Steven M Drucker, Maneesh Agrawala, and Michael F Cohen, “The cartoon animation filter,” in Proceeding of ACM Transactions on Graphics (TOG). ACM, 2006, vol. 25, pp. 1169–1173.
[2]	Ming-Zher Poh, Daniel J McDuff, and Rosalind W Picard, “Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.,” Optics express, vol. 18, no. 10, pp. 10762–10774, 2010.
[3]	Martin Fuchs, Tongbo Chen, Oliver Wang, Ramesh Raskar, Hans-Peter Seidel, and Hendrik PA Lensch, “Real-time temporal shaping of high-speed video streams,” Computers & Graphics, vol. 34, no. 5, pp. 575–584, 2010.
[4]	Yichao Zhang, Silvia L. Pintea, and Jan C. van Gemert, “Video acceleration magnification,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
[5]	Tae-Hyun Oh, Ronnachai Jaroensri, Changil Kim, Mohamed Elgharib, Fr’edo Durand, William T. Freeman, and Wojciech Matusik, “Learning-based video motion magnification,” in The European Conference on Computer Vision (ECCV), September 2018.
[6]	H. Wu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, and W. Freeman, “Eulerian video magnification for revealing subtle changes in the world,” Proceeding of ACM Transactions on Graphics, Volume 31, Number 4 (Proc. SIGGRAPH) 2012.
[7]	Neal Wadhwa, Michael Rubinstein, Fredo Durand, and William T. Freeman, “Phase-based video motion processing,” Proceeding of SIGGRAPH, ACM Transactions on Graphics 2013, vol. 32, no. 4, pp. 80, 2013, https://doi.org/10.1145/2461912.2461966.
[8]	Neal Wadhwa, Michael Rubinstein, Fredo Durand, and William T. Freeman, “Riesz pyramids for fast phase based video magnification,” Proceeding of IEEE International Conference on Computational Photography (ICCP), 2014, DOI: 10.1109/ICCPHOT.2014.6831820.
[9]	Neal Wadhwa, Michael Rubinstein, Fredo Durand, and William T. Freeman, “Quaternionic representation of the riesz pyramid for video magnification,” MIT Computer Science and Artificial Intelligence Laboratory, Technical report, 2014, URI http://hdl.handle.net/1721.1/86300.
[10]	Gamal Fahmy, Omar Fahmy, and Mamdouh Fahmy, “Micro movement magnification in video signals using complex wavelet analysis,” Journal of IET on Image Processing, Vol. 11, Issue 11, November 2017, p. 986 – 993, DOI: 10.1049/iet-ipr.2017.0049.
[11]	Omar Fahmy, Gamal Fahmy, and Mamdouh F. Fahmy, “A new video magnification technique using complex wavelets with radon transform application,” Journal of Signal, Image and Video Processing, June 2018, https://doi.org/10.1007/s11760-018-1306-9. vol. 12, pp. 1505–1512.
[12]	Andrea F. Abate, Michele Nappi, Daniel Riccio, and Gabriele Sabatino, “A survey: 2d and 3d face recognition,” in Pattern Recognition Letters, Pages 1885-1906, Volume 28, Issue 14, 15 October 2007, https://doi.org/10.1016/j.patrec.2006.12.018.
[13]	Eero P Simoncelli and William T Freeman, “The steerable pyramid: A flexible architecture for multi-scale derivative computation,” in Image Processing, 1995. Proceedings., International Conference on. IEEE, 1995, vol. 3, pp. 444–447.
[14]	G. Fahmy, M. Fahmy, and O. M. Fahmy, “Complex wavelet transform cwt based video magnification for 3D facial video identification,” in 2020 International Conference on Computing, Electronics Communications Engineering (iCCECE), 2020, pp. 75–80.
[15]	Gamal Fahmy, Omar Fahmy, and Mamdouh Fahmy, “Fast enhanced dwt based video micro movement magnification,” in Proceeding of IEEE International Symposium on Signal Processing and International Technology, ISSPIT, Dec. 2019, Ajman UAE, DOI: 10.1109/ISSPIT47144.2019.9001874.
[16]	Til Aach and Hartmut Fuhr, “Shift variance measures for multirate lpsv filter banks with random input signals,” IEEE Transaction on Signal Processing, vol. 60, no. 10, pp. 5125–5134, 2012, DOI: 10.1109/TSP.2012.2205683.
[17]	I. Selesnick, R. Baraniuk, and N. Kingsbury, “The dual-tree complex wavelet transform,” Signal Processing Magazine, IEEE, vol. 22, no. 6, pp. 123–151, 2005, DOI: 10.1109/MSP.2005.1550194.
[18]	N. Kingsbury, “A dual-tree complex wavelet transform with improved orthogonality and symmetry properties,” in Proceedings of 2000 IEEE International Conference on Image Processing. DOI: 10.1109/ICIP.2000.899397, 2000, vol. 2, pp. 375–378.
[19]	J. Ng A. A. Bharath, “A steerable complex wavelet construction and its application to image denoising,” Trans. on Image Processing, vol. 14, no. 7, pp. 948–959, 2005.
[20]	Mark JT Smith and Alen Docef, A study guide for Digital Image Processing, Scientific Publishers, 1997.
[21]	M. Pattichis C. Carranze, D. Lllamocca, “Fast and scalable computation of the forward and inverse discrete periodic radon transform,” IEEE Transaction on Image Processing, vol. 25, no. 1, pp. 119–133, 2016, DOI: 10.1109/TIP.2015.2501725.
[22]	Mohamed Elgharib, Mohamed Hefeeda, Fredo Durand, and William T. Freeman, “Video magnification in presence of large motions,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.

Cite This Article

Plain Text BibTeX RIS

APA Style

Gamal Fahmy, Omar Fahmy, Mamdouh Fahmy. (2021). Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification. International Journal of Information and Communication Sciences, 6(3), 75-84. https://doi.org/10.11648/j.ijics.20210603.14

Copy | Download

ACS Style

Gamal Fahmy; Omar Fahmy; Mamdouh Fahmy. Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification. Int. J. Inf. Commun. Sci. 2021, 6(3), 75-84. doi: 10.11648/j.ijics.20210603.14

Copy | Download

AMA Style

Gamal Fahmy, Omar Fahmy, Mamdouh Fahmy. Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification. Int J Inf Commun Sci. 2021;6(3):75-84. doi: 10.11648/j.ijics.20210603.14

Copy | Download

@article{10.11648/j.ijics.20210603.14,
  author = {Gamal Fahmy and Omar Fahmy and Mamdouh Fahmy},
  title = {Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification},
  journal = {International Journal of Information and Communication Sciences},
  volume = {6},
  number = {3},
  pages = {75-84},
  doi = {10.11648/j.ijics.20210603.14},
  url = {https://doi.org/10.11648/j.ijics.20210603.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20210603.14},
  abstract = {Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame sequence, and utilize this in biometric identification. We present a fast 2D Dual Discrete Wavelet Transform 2D-DWT based video magnification technique that detects micro movements by magnifying the phase differences between subsequent video frame's wavelet coefficients, at different sub bands. Next, in order to overcome shortcoming of 2D-DWT systems, 2D Dual Complex Wavelet Transform 2D-CWT has also been employed to estimate phase changes between subsequent video frames at different spatial locations of Complex Wavelets sub-bands. This latter presented CWT Technique uses the Radon Transform to detect any periodic motion in the video frames. Several simulation results are given to show that our proposed hybrid technique achieves comparable and sometimes superior performance with far less complexity when compared with recent literature in micro motion magnification, such as steerable pyramids STR and Riesz Transform RT based steerable pyramids RT-STR. Both DWT and CWT techniques are combined for 3D facial video identification. The attached videos demonstrate the superior video quality obtained by the proposed technique.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification
AU  - Gamal Fahmy
AU  - Omar Fahmy
AU  - Mamdouh Fahmy
Y1  - 2021/09/30
PY  - 2021
N1  - https://doi.org/10.11648/j.ijics.20210603.14
DO  - 10.11648/j.ijics.20210603.14
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 75
EP  - 84
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20210603.14
AB  - Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame sequence, and utilize this in biometric identification. We present a fast 2D Dual Discrete Wavelet Transform 2D-DWT based video magnification technique that detects micro movements by magnifying the phase differences between subsequent video frame's wavelet coefficients, at different sub bands. Next, in order to overcome shortcoming of 2D-DWT systems, 2D Dual Complex Wavelet Transform 2D-CWT has also been employed to estimate phase changes between subsequent video frames at different spatial locations of Complex Wavelets sub-bands. This latter presented CWT Technique uses the Radon Transform to detect any periodic motion in the video frames. Several simulation results are given to show that our proposed hybrid technique achieves comparable and sometimes superior performance with far less complexity when compared with recent literature in micro motion magnification, such as steerable pyramids STR and Riesz Transform RT based steerable pyramids RT-STR. Both DWT and CWT techniques are combined for 3D facial video identification. The attached videos demonstrate the superior video quality obtained by the proposed technique.
VL  - 6
IS  - 3
ER  -

Copy | Download

Author Information

Gamal Fahmy

Electrical Engineering Department, Assiut University, Assiut, Egypt
Omar Fahmy

Electrical Engineering Department, Bader University, Cairo, Egypt
Mamdouh Fahmy

Electrical Engineering Department, Assiut University, Assiut, Egypt

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Gamal Fahmy, Omar Fahmy, Mamdouh Fahmy. (2021). Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification. International Journal of Information and Communication Sciences, 6(3), 75-84. https://doi.org/10.11648/j.ijics.20210603.14

Copy | Download

ACS Style

Gamal Fahmy; Omar Fahmy; Mamdouh Fahmy. Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification. Int. J. Inf. Commun. Sci. 2021, 6(3), 75-84. doi: 10.11648/j.ijics.20210603.14

Copy | Download

AMA Style

Gamal Fahmy, Omar Fahmy, Mamdouh Fahmy. Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification. Int J Inf Commun Sci. 2021;6(3):75-84. doi: 10.11648/j.ijics.20210603.14

Copy | Download

@article{10.11648/j.ijics.20210603.14,
  author = {Gamal Fahmy and Omar Fahmy and Mamdouh Fahmy},
  title = {Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification},
  journal = {International Journal of Information and Communication Sciences},
  volume = {6},
  number = {3},
  pages = {75-84},
  doi = {10.11648/j.ijics.20210603.14},
  url = {https://doi.org/10.11648/j.ijics.20210603.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20210603.14},
  abstract = {Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame sequence, and utilize this in biometric identification. We present a fast 2D Dual Discrete Wavelet Transform 2D-DWT based video magnification technique that detects micro movements by magnifying the phase differences between subsequent video frame's wavelet coefficients, at different sub bands. Next, in order to overcome shortcoming of 2D-DWT systems, 2D Dual Complex Wavelet Transform 2D-CWT has also been employed to estimate phase changes between subsequent video frames at different spatial locations of Complex Wavelets sub-bands. This latter presented CWT Technique uses the Radon Transform to detect any periodic motion in the video frames. Several simulation results are given to show that our proposed hybrid technique achieves comparable and sometimes superior performance with far less complexity when compared with recent literature in micro motion magnification, such as steerable pyramids STR and Riesz Transform RT based steerable pyramids RT-STR. Both DWT and CWT techniques are combined for 3D facial video identification. The attached videos demonstrate the superior video quality obtained by the proposed technique.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification
AU  - Gamal Fahmy
AU  - Omar Fahmy
AU  - Mamdouh Fahmy
Y1  - 2021/09/30
PY  - 2021
N1  - https://doi.org/10.11648/j.ijics.20210603.14
DO  - 10.11648/j.ijics.20210603.14
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 75
EP  - 84
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20210603.14
AB  - Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame sequence, and utilize this in biometric identification. We present a fast 2D Dual Discrete Wavelet Transform 2D-DWT based video magnification technique that detects micro movements by magnifying the phase differences between subsequent video frame's wavelet coefficients, at different sub bands. Next, in order to overcome shortcoming of 2D-DWT systems, 2D Dual Complex Wavelet Transform 2D-CWT has also been employed to estimate phase changes between subsequent video frames at different spatial locations of Complex Wavelets sub-bands. This latter presented CWT Technique uses the Radon Transform to detect any periodic motion in the video frames. Several simulation results are given to show that our proposed hybrid technique achieves comparable and sometimes superior performance with far less complexity when compared with recent literature in micro motion magnification, such as steerable pyramids STR and Riesz Transform RT based steerable pyramids RT-STR. Both DWT and CWT techniques are combined for 3D facial video identification. The attached videos demonstrate the superior video quality obtained by the proposed technique.
VL  - 6
IS  - 3
ER  -

Copy | Download