CV Lattes
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Universidade Estadual Paulista (UNESP). Campus de Bauru. Faculdade de Ciências (FC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Graduated in Mathematics by the IBILCE-UNESP, S.J. Rio Preto, in 1986. Master in Computer Science by the Computing Institute of UNICAMP, Campinas, in the area of Programming Languages, in 1990. Doctor in Electrical Engineering by the Faculty of Electrical and Computer Engineering of UNICAMP, Campinas, in 1997. He completed a sandwich doctorate at King's College London, London, UK, from September 1995 to January 1997, in Computer Vision. He received a postdoctoral fellowship at Michigan State University, East Lansing, MI, USA, from August 2004 to July 2005, with Professor Anil K. Jain , in the area of Biometrics. He was the head of the Computer Science Department, Unesp, Bauru, from July 2002 to July 2004. He was coordinator of the lato-sensu postgraduate courses, CEInfo and CETI, Faculty of Sciences, Unesp, Bauru, in the years 2000, 2001, 2006 and 2007. He was president of the Local Evaluation Group of the Faculty of Sciences, Unesp, Bauru, in the Institutional Evaluation process of Unesp, during the triennium 2006-2008. He was the coordinator of the Bachelor's Degree in Computer Science, Unesp, Bauru, from April 2008 to April 2010, and from September 2018 to August 2022. He was coordinator of the Post-Graduation Program in Computer Science at Unesp from June 2010 to April 2014. (Source: Lattes Curriculum)
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(Only some records are available in English at this moment)
Human pose estimation in images is an important and challenging problem in Computer Vision. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. 2D poses can be used in a diverse and broad set of applications, of great relevance to society. However, the use of 3D poses can bring even more accurate and robust results. The objective of this …
In recent decades, biometrics has become an important tool for identifying individuals in the most diverse areas, used to prevent fraud and increase the security of citizens in society. Current biometric systems are mostly based on the analysis of physical characteristics such as fingerprints and faces. Despite the good results obtained, resulting from the most recent advances in this are…
In the last decades, for reasons of safety or convenience, biometric characteristics are increasingly being used to identify individuals who wish to have access to systems or places, and facial features are one of the most used characteristics for this purpose. For biometric identification to be effective, the rates of recognition accuracy must be high. However, these rates can be very lo…
(Only some records are available in English at this moment)
Monocular human pose estimation is an important Computer Vision problem, aimed to estimate the human body shape based on a single RGB image. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation, which can be used in a diverse set of applications. However, the use of 3D poses can bring more accurate and robust results. As 3D pose annotations…
The problem of fine-tuning parameters in deep learning techniques has been considerably focused in the last years, since to hand-tune them is painful and prone to errors. In this proposal, we model the problem of adjusting parameters as an optimization task, since meta- heuristic techniques have obtained very interesting results in a number of problems. There is one inspired on the behavi…
Deep learning techniques have been widely used in the last years due to their promising results in several applications, mainly face and object detection. However, one of their main shortcomings is related to the selection of suitable parameters in order to allow reasonable results. Since we may have millions of parameters, a manual fine tuning of them seems to be impractical. In this res…
(Only some records are available in English at this moment)
| 13 / 9 | Completed research grants |
| 20 / 11 | Completed scholarships in Brazil |
| 5 / 4 | Completed scholarships abroad |
| 38 / 24 | All research grants and scholarships |
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