Published February 28, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Human Face Reconstruction using Divine Proportions and Gestalt for Occluded Video Face Recovery in Forensic Analysis using Deep Learning

Creators

  • 1. Department of Computer Science, B.M.S. College for Women, Basavangudi, Bangalore (Karnataka), India

Contributors

Contact person:

Researcher:

  • 1. Department of Computer Science, B.M.S. College for Women, Basavangudi, Bangalore (Karnataka), India
  • 2. Department of Computer Science, Arulmigu Arthanareeswarar Arts and Science College, Thiruchengodu (Tamil Nadu), India.

Description

Abstract: Forensic video analysis has been used in diverse kind of high-profile cases, global discrepancies, and conflict zones. It is a three-phase process of scientific examination, comparison, and evaluation of video in legal matters. Human facereconstruction using deep learning for occluded video face recovery to aid in forensic analysis is the main objective of this paper. Forensic facial reconstruction is a combination of both scientific methods and artistic skill. In this paper, we introduce a method to reconstruct human faces occluded due to short noise innight-time video clips. A skull database is created with unique skull models with varying shapes, forms and proportions. Human body mathematical model biometric using golden ratio algorithm is proposed and used to find the occluded face proportions. Closure principle of gestalt theory of visual perception is used to fill in the missing parts of a face design and to create a whole faceimage using gan. The proposed model isfound to have 50% lesserreduced Median error rate and 20% reduced Stdev than PrNet and 10% lower Mean error rate than 3Dddfav2.

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Additional details

Identifiers

DOI
10.35940/ijies.B1089.11020224
EISSN
2319-9598

Dates

Accepted
2024-02-15
Manuscript received on 30 January 2024 | Revised Manuscript received on 08 February 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024.

References

  • Lee WJ, Wilkinson CM, Hwang H. An Accuracy assessment of forensic computerized facial reconstruction employing cone-beam computed tomography from live subjects. J Forensic Sci. 2012;57(2):318-27. https://doi.org/10.1111/j.1556- 4029.2011.01971.x
  • Fernandes CM, SeeraMda C, da Silva JV, Noritomi PY, Pereira FD,Melani RF. Tests of one Brazilian facial reconstruction method using three soft tissue depth sets and familiar ascessors. Forensic Sci Int. 2012;214:211e1- 1e7. https://doi.org/10.1016/j.forsciint.2011.08.017
  • Cavanagh D, Steyn M. Facial reconstruction: Soft tissue thickness values for South African black females. Forensic Sci Int. 2011;206:215e1-e7 https://doi.org/10.1016/j.forsciint.2011.01.009
  • Sonia Gupta, 1 Vineeta Gupta,2 Hitesh Vij,3 Ruchieka Vij,4 and Nutan Tyagi5, Forensic Facial Reconstruction: The Final Frontier, 2015, doi: 10.7860/JCDR/2015/14621.6568 https://doi.org/10.7860/JCDR/2015/14621.6568
  • Mahalanobis, Prasanta Chandra (1936). "On the generalised distance in statistics" (PDF). Proceedings of the National Institute of Sciences of India. 2 (1): 49–55. Retrieved 2016-09-27.
  • Wilkinson CM. Facial reconstruction- anatomical art or artistic anatomy? J Anat. 2010;216(2):235–50. https://doi.org/10.1111/j.1469-7580.2009.01182.x
  • Yadav N, Panat RS, Aggarwal A. CT scans- a compelling tool in forensic facial reconstruction. J Dent Sci Oral Rehabil. 2010;1(39):42.
  • Wilkinson C, Rynn C, Peters H, Taister M, Kau CH, Richmond S. A blind accuracy assessment of computer-modeled forensic facial reconstruction using computer tomography data from live subjects. Forensic Sci Med Pathol. 2006;2:179– 88. https://doi.org/10.1007/s12024-006-0007-9
  • Turner W, Tu P, Kelliher T, Brown R. Computer-aided forensics: facial reconstruction. Stud Health Technol Inform. 2006;119:550–55.
  • Kreutz K, Verhoff MA. Forensic Facial Reconstruction-Identification based on skeletal finding. DtschArztebl. 2007;104(17):A1160-65.
  • Abate AF, Nappi M, Tortora RG. FACES: 3D facial reconstruction from ancient skulls using content based image retrieval. JVis Lang Comput. 2004;15:373- 89. https://doi.org/10.1016/j.jvlc.2003.11.004
  • Fernandes CM, Pereira FD, da Silva JV, Serra Mda C. Is characterizing the digital forensic facial reconstruction with hair necessary? A familiar asssessors' analysis. Forensic Sci Int. 2013;229:164.e1-e5 https://doi.org/10.1016/j.forsciint.2013.03.036
  • Kahler K, Haber J, Seidel H. Re-animating the dead: Reconstruction of expressive faces from skull data. ACM TOG. 2003;22(3):554-61 https://doi.org/10.1145/882262.882307
  • Omstead J. Facial reconstruction. Uni West Ont Anthrol. 2011;10(1):37-46 https://doi.org/10.5206/uwoja.v10i1.8803
  • Reichs and Craig. Facial Approximation: Procedures and Pitfalls.
  • Fast 3D face reconstruction from a single image combining attention mechanism and graph convolutional network. Vis Comput 39, 5547– 5561 (2023). https://doi.org/10.1007/s00371-022-02679-9
  • Wilkinson, Caroline (2010). "Facial reconstruction – anatomical art or artistic anatomy?". Journal of Anatomy. 216 (2): 235– 250. doi:10.1111/j.1469-7580.2009.01182.x. ISSN 0021 8782. PMC 2815945. PMID 20447245. https://doi.org/10.1111/j.1469-7580.2009.01182.x
  • FORENSIC ANTHROPOLOGY: FACIAL RECONSTRUCTION, C13T_Facial-Reconstruction.pdf
  • https://github.com/microsoft/DigiFace1M.git
  • VGGFace2: A dataset for recognising faces across pose and age, Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi and Andrew Zisserman.
  • Generative adversarial networks, ian j. Goodfellow, jean ouget-abadie, mehdi mirza, bing xu, david warde-farley, sherjil ozair, aaron courville, yoshua bengio
  • https://medium.com/data-and-beyond/vectordatabase
  • Leonardo da Vinci's Vitruvian Man: The Ideal Human Proportions and Man as a Measure of All Things, Oranges, Carlo M. M.D.; Largo, René D. M.D.; Schaefer, Dirk J. M.D.
  • Guberman, Shelia. (2015). On Gestalt Theory Principles.
  • An Introduction to Convolutional Neural Networks Keiron O'Shea 1 and Ryan Nash 2
  • Forensic Facial Reconstruction, Caroline Wilkinson, University of Dundee, 01 Jan 2004.
  • Forensic Facial Reconstruction Using Mesh Template Deformation with Detail Transfer over HRBF, Rafael Romeiro, Ricardo Marroquim1, Claudio Esperança1, Andreia Breda.
  • Forensic facial reconstruction – between art and science, Ljerka Polić, Anja Petaros, Dražen Cuculić, Alan Bosnar, 01 Mar 2012-Vol. 48, Iss: 1, pp 30-40..
  • Forensic Facial Reconstruction: The history of facial reconstruction, Caroline Wilkinson Institutions (1), May 2004. https://doi.org/10.1017/CBO9781107340961.002
  • Superimposition-guided Facial Reconstruction from Skull, Celong Liu, Xin Li.
  • Deep Face Feature for Face Alignment and Reconstruction Boyi Jiang, Juyong Zhang, Bailin Deng, Yudong Guo and Ligang Liu.
  • L. Soni, A. Kaur and A. Sharma, "A Review on Different Versions and Interfaces of Blender Software," 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2023, pp. 882-887, doi: 10.1109/ICOEI56765.2023.10125672. https://doi.org/10.1109/ICOEI56765.2023.10125672
  • Wilkinson CM. Facial reconstruction- anatomical art or artistic anatomy? J Anat. 2010;216(2):235–50. https://doi.org/10.1111/j.1469-7580.2009.01182.x
  • Yadav N, Panat RS, Aggarwal A. CT scans- a compelling tool in forensic facial reconstruction. J Dent Sci Oral Rehabil. 2010;1(39):42.
  • Wilkinson C, Rynn C, Peters H, Taister M, Kau CH, Richmond S. A blind accuracy assessment of computer-modeled forensic facial reconstruction using computer tomography data from live subjects. Forensic Sci Med Pathol. 2006;2:179– 88. https://doi.org/10.1007/s12024-006-0007-9
  • Turner W, Tu P, Kelliher T, Brown R. Computer-aided forensics: facial reconstruction. Stud Health Technol Inform. 2006;119:550–55
  • Ali, G. S. H., & NITHYA, Dr. A. (2019). Modelling an Adaptive Cluster Head Positioning Based Map Reducing Strategy for Data Transmission in Medical IoT. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 78–79). https://doi.org/10.35940/ijeat.a1000.109119
  • Meesala., S. R., & V.N.A, N. (2019). A novel framework for Time Dependent Availability Analysis of Degraded Systems. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 23–26). https://doi.org/10.35940/ijrte.c3861.098319
  • Wardhana, M. H., Basari, Prof. Dr. A. S. H., Mohd Jaya, Dr. A. S., Afandi, Prof. Dr. dr. D., & Dzakiyullah, N. R. (2019). A Hybrid Model using Artificial Neural Network and Genetic Algorithm for Degree of Injury Determination. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2, pp. 1357– 1365). https://doi.org/10.35940/ijitee.b6169.129219