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Face Classification: A Specialized Benchmark Study

  • Jiali Duan
  • Shengcai Liao
  • Shuai Zhou
  • Stan Z. Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9967)

Abstract

Face detection evaluation generally involves three steps: block generation, face classification, and post-processing. However, firstly, face detection performance is largely influenced by block generation and post-processing, concealing the performance of face classification core module. Secondly, implementing and optimizing all the three steps results in a very heavy work, which is a big barrier for researchers who only cares about classification. Motivated by this, we conduct a specialized benchmark study in this paper, which focuses purely on face classification. We start with face proposals, and build a benchmark dataset with about 3.5 million patches for two-class face/non-face classification. Results with several baseline algorithms show that, without the help of post-processing, the performance of face classification itself is still not very satisfactory, even with a powerful CNN method. We’ll release this benchmark to help assess performance of face classification only, and ease the participation of other related researchers.

Keywords

Face detection Face classification Benchmark evaluation 

Notes

Acknowledgements

This work was supported by the National Key Research and Development Plan (Grant No.2016YFC0801002), the Chinese National Natural Science Foundation Projects #61473291, #61572501, #61502491, #61572536, NVIDIA GPU donation program and AuthenMetric R&D Funds.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jiali Duan
    • 1
  • Shengcai Liao
    • 2
  • Shuai Zhou
    • 3
  • Stan Z. Li
    • 2
  1. 1.School Of Electronic, Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of AutomationUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Macau University of Science and TechnologyTaipaMacau

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