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MSc StudentNov 2015 - Jul 2016 (8 months)
Masther Thesis Student in 3D Face Recognition Under Unconstrained Settings Using Low-Cost Sensors
Scholarship ResearcherApr 2015 - Sep 2015 (5 months)
Research work in CT Lung images, where I worked on juxta-vascular nodule detection.
Integreated Master in Bioengineering - Biomedical Engineering2011 - 2016 (5 years)
An improved method for juxta-vascular nodule candidate detection
In this paper we propose a new 3D Hessian based medialness filter for the candidate detection phase in order to improve the quality of the juxta-vascular nodules that were identified as a problem in some recent approaches. Our approach shows a significant improvement for the juxta-vascular cases, by having a considerable reduction onthe number of false positives (FP) when comparing with other methods.
Multimodal Hierarchical Face Recognition using Information from 2.5D Images
In this paper we propose a multimodal extension of a previous work, based on SIFT descriptors of RGB images, integrated with LBP information obtained from depth scans, modeled by an hierarchical framework motivated by principles of human cognition. The framework was tested on EURECOM dataset and proved that the inclusion of depth information improved significantly the results in all the tested conditions, compared to independent unimodal approaches.
A Comparative analysis of deep and shallow features for multimodal face recognition
We propose a new RGB-depth-infrared (RGB-D-IR) dataset, RealFace, acquired with the novel Intel RealSense collection of sensors, and characterized by multiple variations in pose, lighting and disguise. We conclude that our dataset presents some relevant challenges and that deep feature descriptors present both higher robustness in RGB images, as well as an interesting margin for improvement in alternative sources, such as depth and IR.