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.