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A Systematic Training Procedure for Viola-Jones Face Detector in Heterogeneous Computing Architecture

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Abstract

The face detection has become one of the most important topics in machine learning and computer vision in the last few decades. Many papers have been published utilizing various methods for face detection. One of the most popular face detectors used in many practical applications with heterogeneous computing architecture is Viola-Jones method. Despite of being a real-time and robust face detector, it suffers from not well explained parts at the training procedure, e.g., not clear how to select few features in the first cascades or not clear how many samples are needed and how to gather a good trainset for training a cascade. In this paper, a trainset selection method based on histograms generated from AdaBoost and in addition, simple ways to select few features in beginning cascades are proposed. The training procedure is then compared to a baseline training presented in the previous studies.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of California, Merced, USA

    Pooya Tavallali

  2. Department of Communications and Electronic Engineering, Shiraz University, Shiraz, Iran

    Mehran Yazdi

  3. Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran

    Mohammad R. Khosravi

Authors
  1. Pooya Tavallali
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  2. Mehran Yazdi
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  3. Mohammad R. Khosravi
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Correspondence to Pooya Tavallali.

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Tavallali, P., Yazdi, M. & Khosravi, M.R. A Systematic Training Procedure for Viola-Jones Face Detector in Heterogeneous Computing Architecture. J Grid Computing 18, 847–862 (2020). https://doi.org/10.1007/s10723-020-09517-z

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  • DOI: https://doi.org/10.1007/s10723-020-09517-z

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