3D Face Reconstruction with Feature Enhancement using Bi-FPN for Forensic Analysis

Title

3D Face Reconstruction with Feature Enhancement using Bi-FPN for Forensic Analysis

Creator

John S.; Danti A.

Description

The representation of facial features in three-dimensional space plays a pivotal role in various applications such as facial recognition, virtual reality, and digital entertainment. However, achieving high-fidelity reconstructions from two-dimensional facial images remains a challenging task, particularly in preserving fine texture details. This research addresses this problem by proposing a novel approach that leverages a combination of advanced techniques, including Resnet, Flame model, Bi-FPN, and a differential render architecture. The primary objective of this study is to enhance texture details in reconstructed 3D facial images. The integration of Bi-FPN (Bi-directional Feature Pyramid Network) enhances feature extraction and fusion across multiple scales, facilitating the preservation of texture details across different regions of the face. The objective is to accurately represent facial features from 2D images in three-dimensional space. By combining these methods, the proposed framework achieves significant improvements in preserving fine texture details and overall facial structure. Experimental results demonstrate the effectiveness of the approach, suggesting its potential for various applications such as virtual try-on and facial animation. 2024 The Authors.

Source

Journal of Machine and Computing, Vol-4, No. 2, pp. 392-399.

Date

2024-01-01

Publisher

AnaPub Publications

Subject

3D Face Reconstruction; Bi-FPN; Flame Model; Resnet

Coverage

John S., Department of Computer Science and Engineering, Christ University, Bangalore, India; Danti A., Department of Computer Science and Engineering, Christ University, Bangalore, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 27891801

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.53759/7669/jmc202404037

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190324219&doi=10.53759%2f7669%2fjmc202404037&partnerID=40&md5=7f25a2d78032b904008c72074402a324