Vinayaka R Kamath

Vinayaka R Kamath
People's Education Society | PES · Computer Science

Bachelor of Technology

About

9
Publications
2,769
Reads
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3
Citations
Citations since 2017
9 Research Items
3 Citations
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
Additional affiliations
July 2021 - present
Microsoft
Position
  • Research Associate
January 2020 - June 2020
Microsoft
Position
  • Research Intern
June 2019 - July 2019
Samsung R&D
Position
  • Student Trainee
Education
August 2016 - May 2020
People's Education Society
Field of study
  • Computer Science and Engineering

Publications

Publications (9)
Preprint
Full-text available
Identification of an entity that is of interest is prominent in any intelligent system. The visual intelligence of the model is enhanced when the capability of recognition is added. Several methods such as transfer learning and zero shot learning help to reuse the existing models or augment the existing model to achieve improved performance at the...
Chapter
Full-text available
Security and identity have become one of the primary concerns of the people in this digital world. Person authentication and identification is transforming the way these services are provided. Earlier it was mainly achieved through passwords and patterns but with significant advancements in face recognition technologies, it has been exploited in pr...
Chapter
Full-text available
Face recognition is prevailing to be a key aspect wherever there is a need for interaction between humans and machines. This can be achieved by containing a set of sketches for all the possible individuals and then cross-validating at necessary circumstances. We propose a mechanism to fulfil this task which is centred on locally adaptive regression...
Article
Full-text available
The paper presents several thresholds obtained by heuristic approach for face verification using Locally Adaptive Regression Kernel (LARK) descriptors for euclidean, cosine and chebyshev distance metrics. The absence of a threshold for several distance metrics possess several setbacks such as increased computational complexity and escalated runtime...

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Projects

Project (1)
Project
Generate a summary of a Kannda extract in English.