Mathematics and Computational Sciences

Generative Adversarial Network-Based Forensic Facial Reconstruction from 3D Face Meshes: A Comparative Study of DCGAN and StyleGAN

Articles in Press

Document Type : Original Article

Authors

Kalyani T 1
Meena Devi G 2
Gayathri Lakshmi M 3
Immanuel Prabaharan S 4

1 Department of Mathematics St. Joseph’s Institute of Technology Chennai, Tamil Nadu, India

2 Department of Mathematics, St. Joseph's College of Engineering, Chennai, Tamil Nadu, India

3 Department of Mathematics, Saveetha Engineering college, Chennai, Tamil Nadu, India

4 AMET University, Kanathur, ECR, Chennai, Tamil Nadu, India

https://doi.org/10.30511/mcs.2026.2077705.1621
Abstract
Forensic facial reconstruction is a critical investigative technique for identifying unknown individuals from skeletal remains, facial meshes, or degraded photographs. Existing approaches manual clay reconstruction, drag-and-drop composite software, and Shape-from-Shading algorithms are time consuming, cost-prohibitive, or limited to well-defined facial inputs. This paper proposes an automated, generative AI based pipeline that reconstructs realistic facial images from 3-D face mesh representations and 2-D facial sketches using Deep Convolutional Generative Adversarial Networks (DCGAN) and Style-based Generative Adversarial Networks (StyleGAN). A dedicated dataset of 5 000 paired mesh-to-face image samples was constructed using Google MediaPipe, covering diverse age groups, ethnicities, and genders with an 80/10/10 train-validationtest split. DCGAN achieved a testing accuracy of 94.2% at 300 epochs, while StyleGAN attained a Fr´echet Inception Distance (FID) of 18.3 at 200 epochs with SSIM = 0.78 and PSNR = 28.1 dB. Comparative evaluation shows that DCGAN excels at rapid broad-pattern reconstruction, while StyleGAN provides superior feature-level perceptual fidelity. Ethical risks including misidentification, dataset consent, demographic bias, and legal implications are also addressed.

Keywords

Facial Reconstruction
StyleGAN
DCGAN
Forensic AIF
Subjects
Mathematics and Computational Sciences

Articles in Press, Accepted Manuscript
Available Online from 20 June 2026

  • Receive Date 14 November 2025
  • Revise Date 20 June 2026
  • Accept Date 20 June 2026

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