Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/101231
Title: Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis
Authors: Jin, Bo 
Cruz, Leandro 
Gonçalves, Nuno 
Keywords: Facial diagnosis; deep transfer learning (DTL); face recognition; beta-thalassemia; hyperthyroidism; down syndrome; leprosy
Issue Date: 2020
Project: Institute of Systems and Robotics (ISR), and the Portuguese Mint and Official Printing Office (INCM) under Grant FACING: BI-BOLSA1 
Serial title, monograph or event: IEEE Access
Volume: 8
Abstract: The relationship between face and disease has been discussed from thousands years ago, which leads to the occurrence of facial diagnosis. The objective here is to explore the possibility of identifying diseases from uncontrolled 2D face images by deep learning techniques. In this paper, we propose using deep transfer learning from face recognition to perform the computer-aided facial diagnosis on various diseases. In the experiments, we perform the computer-aided facial diagnosis on single (beta-thalassemia) and multiple diseases (beta-thalassemia, hyperthyroidism, Down syndrome, and leprosy) with a relatively small dataset. The overall top-1 accuracy by deep transfer learning from face recognition can reach over 90% which outperforms the performance of both traditional machine learning methods and clinicians in the experiments. In practical, collecting disease-speci c face images is complex, expensive and time consuming, and imposes ethical limitations due to personal data treatment. Therefore, the datasets of facial diagnosis related researches are private and generally small comparing with the ones of other machine learning application areas. The success of deep transfer learning applications in the facial diagnosis with a small dataset could provide a low-cost and noninvasive way for disease screening and detection.
URI: https://hdl.handle.net/10316/101231
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3005687
Rights: openAccess
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais
FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais

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