Jin Bo was born in Nanjing, China. He received both his B.Sc. and M.Sc. degrees from the Department of Electrical and Computer Engineering, University of Macau, Macau SAR, China. He earned his Ph.D. degree from the Department of Electrical and Computer Engineering, University of Coimbra - Alta and Sofia, Coimbra, Portugal. He conducted his doctoral research and was conducting his post-doctoral research with the Visual Information Security Team at the Institute of Systems and Robotics, University of Coimbra, Portugal.He published the research results related to 'Deep Facial Diagnosis', which was awarded a national invention patent by the People’s Republic of China (PRC). He has a broad spectrum of research interests, with a particular focus on computers, genetics, and robotics.
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Publications
Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis
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-specific 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.
- Date: 16/06/2020
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- Featured In: IEEE Access, vol. 8, pp. 123649-123661
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- Publication Type: Journal Articles
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- Author(s): Bo Jin, Leandro Cruz, Nuno Gonçalves
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- DOI: 10.1109/ACCESS.2020.3005687
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Pseudo RGB-D Face Recognition
In the last decade, advances and popularity of low cost RGB-D sensors have enabled us to acquire depth information of objects. Consequently, researchers began to solve face recognition problems by capturing RGB-D face images using these sensors. Until now, it is not easy to acquire the depth of human faces because of limitations imposed by privacy policies, and RGB face images are still more common. Therefore, obtaining the depth map directly from the corresponding RGB image could be helpful to improve the performance of subsequent face processing tasks such as face recognition. Intelligent creatures can use a large amount of experience to obtain three-dimensional spatial information only from two-dimensional plane scenes. It is machine learning methodology which is to solve such problems that can teach computers to generate correct answers by training. To replace the depth sensors by generated pseudo depth maps, in this paper, we propose a pseudo RGB-D face recognition framework and provide data driven ways to generate the depth maps from 2D face images. Specially, we design and implement a generative adversarial network model named “D+GAN” to perform the multi-conditional image- to-image translation with face attributes. By this means, we validate the pseudo RGB-D face recognition with experiments on various datasets. With the cooperation of image fusion technologies, especially Non-subsampled Shearlet Transform, the accuracy of face recognition has been signi cantly improved.
- Date: 01/08/2022
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- Featured In: IEEE Sensors Journal
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- Publication Type: Journal Articles
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- Author(s): Bo Jin, Leandro Cruz and Nuno Gonçalves
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- DOI: 10.1109/JSEN.2022.3197235
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Face depth prediction by the scene depth
Depth map, also known as range image, can directly reflect the geometric shape of the objects. Due to several issues such as cost, privacy and accessibility, face depth information is not easy to obtain. However, the spatial information of faces is very important in many aspects of computer vision especially in the biometric identification. In contrast, scene depth information is related easier to obtain with the development of autonomous driving technology in recent years. An idea of face depth estimation inspired is to bridge the gap between the scene depth and the face depth. Previously, face depth estimation and scene depth estimation were treated as two completely separate domains. This paper proposes and explores utilizing scene depth knowledge learned to estimate the depth map of faces from monocular 2D images. Through experiments, we have preliminarily verified the possibility of using scene depth knowledge to predict the depth of faces and its potential in face feature representation.
- Date: 23/06/2021
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- Featured In: IEEE/ACIS 20th International Conference on Computer and Information Science, Shanghai, China
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- Publication Type: Conference Papers
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- Author(s): Bo Jin, Leandro Cruz, Nuno Gonc ̧alves
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- DOI: 10.1109/ICIS51600.2021.9516598
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