Face Classification: A Specialized Benchmark Study
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.
KeywordsFace detection Face classification Benchmark evaluation
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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