Simulated multimodal deep facial diagnosis; Deep facial diagnosisSimulated multimodalFace depth estimationFacial phenotypesCondition-specific facesBilinear

Type of Publication

Journal Articles

Date:

10 /

2024

Status

Published

DOI:

10.1016/j.eswa.2024.123881

Simulated multimodal deep facial diagnosis

Featured in:

Expert Systems with Applications

Authors:

Bo Jin, Nuno Gonçalves, Leandro Cruz, Iurii Medvedev, Yuanyu Yu and Jiujiang Wang

Abstract

Facial phenotypes are extensively studied in medical and biological research, serving as critical markers that potentially indicate underlying genetic traits or medical conditions. With the recent advancements in big data, algorithms, and hardware, deep facial diagnosis, which employs deep learning techniques to systematically examine facial phenotypes and identify signs of certain diseases or medical conditions, has attracted significant attention and research, gradually emerging as a promising tool in precision medicine. Primarily limited by the scarcity of data for training facial diagnosis models, the accuracy of facial diagnosis for various conditions remains low up to now. In the past decade, RGB-D cameras, measuring depth information along with standard RGB capabilities, have proven superior in processing spatial details with more stability and accuracy. Motivated by the facts mentioned above, in this paper, we propose a Simulated Multimodal Framework, which effectively improves the computer-aided facial diagnosis performance of state-of-the-art models in experiments under different conditions. The underlying principle is to leverage the simulated depth by generative models to improve the performance of RGB image recognition. Furthermore, as a rapid and non-invasive tool for disease screening and detection, our proposal demonstrated an average accuracy improvement of over 20% compared to practicing physicians in the study.

Citation
Bo Jin, Nuno Gonçalves, Leandro Cruz, Iurii Medvedev, Yuanyu Yu and Jiujiang Wang. Simulated multimodal deep facial diagnosis. Expert Systems with Applications, Volume 252, Part A, 2024. DOI: 10.1016/j.eswa.2024.123881.

Related Content

Researcher Coordinator, VIS TEAM Leader
Post-Doc Researcher
Researcher
Post-Doc Researcher
No tagged content to show
No tagged content to show
No tagged content to show

RECENT PUBLICATIONS

VOIDFace: Towards an Effective Face Training Data Storage and Protection with Right to be Forgotten Property

Authors: Ajnas Muhammed; Iurii Medvedev; Nuno Gonçalves
Featured in: IEEE International Joint Conference on Biometrics (IJCB 2025)

Análise de marcadores biométricos e sinais vitais através de visão artificial: Ritmo Cardíaco

Authors: Rodrigo Rosado Gonçalves
Featured in: Bsc Thesis

How to Evaluate a Biometric Facial Image

Authors: Diana Gonçalves D’Amil
Featured in: Bsc Thesis

suggested news

Paper accepted to IJCB 2025
Prof. Nuno and VIS Team successfully organizes IbPRIA...
Four papers presented @ IbPRIA 2025

RECENT PROJECTS

FACING2 – Face Image Understanding
VISUAL-ID – Unique Visual Identities in Graphics, Images and Faces
UniqueMark

Institute of Systems and Robotics Department of Electrical and Computers Engineering University of Coimbra