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Skull2Face: Anatomy-Guided 3D Facial Reconstruction System using Deep Learning and Tissue Depth Modeling

by Aiswarya Michael, Akash Reji, Anjali Raju, Serin Mariyam Varghese, Rasmi P.S.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 80
Year of Publication: 2026
Authors: Aiswarya Michael, Akash Reji, Anjali Raju, Serin Mariyam Varghese, Rasmi P.S.
10.5120/ijca2026926385

Aiswarya Michael, Akash Reji, Anjali Raju, Serin Mariyam Varghese, Rasmi P.S. . Skull2Face: Anatomy-Guided 3D Facial Reconstruction System using Deep Learning and Tissue Depth Modeling. International Journal of Computer Applications. 187, 80 ( Feb 2026), 45-49. DOI=10.5120/ijca2026926385

@article{ 10.5120/ijca2026926385,
author = { Aiswarya Michael, Akash Reji, Anjali Raju, Serin Mariyam Varghese, Rasmi P.S. },
title = { Skull2Face: Anatomy-Guided 3D Facial Reconstruction System using Deep Learning and Tissue Depth Modeling },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2026 },
volume = { 187 },
number = { 80 },
month = { Feb },
year = { 2026 },
issn = { 0975-8887 },
pages = { 45-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number80/skull2face-anatomy-guided-3d-facial-reconstruction-system-using-deep-learning-and-tissue-depth-modeling/ },
doi = { 10.5120/ijca2026926385 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-02-21T01:27:55.352414+05:30
%A Aiswarya Michael
%A Akash Reji
%A Anjali Raju
%A Serin Mariyam Varghese
%A Rasmi P.S.
%T Skull2Face: Anatomy-Guided 3D Facial Reconstruction System using Deep Learning and Tissue Depth Modeling
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 80
%P 45-49
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

3D face reconstruction from skull plays a crucial role in forensic identification and anthropology. Especially when landslides produce damaged or partially occluded human skulls, it is important to use 3D face reconstruction so that the family can identify the person. Conventional methods have been manual, subjective, and likely to take a long period of time. This paper proposes a system Skull2Face that receives the image of a skull, and generates a realistic 3D face structure using deep learning methods and tissue depth creation by using generative diffusion models. The method we propose combines anatomical landmarks with statistical modeling for accuracy while being able to create a detailed, personalized output with a realistic texture. This modular method lessens the dependence on large databases, introduces an efficient, faster, individualized, automated alternative methodology for facial reconstruction, and improves Identity-consistency, realism and accuracy over earlier methods of forensic facial reconstruction.

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Index Terms

Computer Science
Information Sciences

Keywords

3D face reconstruction deep learning tissue depth creation generative diffusion models

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