Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection

Authors

  • Germán Ezequiel Lescano Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET )
  • Pablo Santana Mansilla Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
  • Rosanna Costaguta Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

Keywords:

feature selection, CUDA, Viola-Jones, Algorithm, Adaboost

Abstract

Faces and facial expressions recognition is an interesting topic for researchers in machine vision. Viola-Jones algorithm is the most spread algorithm for this task. Building a classification model for face recognition can take many years if the implementation of its training phase is not optimized. In this study, we analyze different implementations for the training phase. The aim was to reduce the time needed during training phase when using one computer with a cheap graphical processing unit (GPU). The execution times were analyzed and compared with previous studies. Results showed that combining C language, CUDA, etc., it is possible to reach acceptable times for training phase. Further research may involve the measurement of the performance of our approach computers with better GPU capacity and exploring a multi-GPU approach.

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References

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Published

2017-04-01

How to Cite

Lescano, G. E., Santana Mansilla, P., & Costaguta, R. (2017). Analysis of a GPU implementation of Viola-Jones’ Algorithm for Features Selection. Journal of Computer Science and Technology, 17(01), p. 68–73. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/449

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Section

Invited Articles