Face Classification: A Specialized Benchmark Study
- 2.4k Downloads
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.
- 1.Kostinger, M., Wohlhart, P., Roth, P.M., et al.: Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2144–2151. IEEE (2011)Google Scholar
- 2.Jain, V., Erik Learned-Miller, F.: A benchmark for face detection in unconstrained settings. Technical Report: UM-CS-2010-009 (2010)Google Scholar
- 3.Yang, S., Luo, P., Loy, C.C., Tang, X., WIDER FACE: a face detection benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
- 4.Zhu, X., Ramanan, D.: Face detection, pose estimation, landmark localization in the wild. In: Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
- 6.The CIFAR-10 dataset. https://www.cs.toronto.edu/~kriz/cifar.html
- 8.Liao, S., Hu, Y., Zhu, X., et al.: Person re-identification by local maximal occurrence representation, metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)Google Scholar
- 9.Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
- 10.Jiang, H., Learned-Miller, E.: Face detection with the faster R-CNN. arXiv preprint (2016). arXiv:1606.03473
- 11.Li, H., Lin, Z., Shen, X., et al.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)Google Scholar
- 17.Van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., et al.: Segmentation as selective search for object recognition. In: International Conference on Computer Vision, pp. 1879–1886. IEEE (2011)Google Scholar
- 19.Arbelez, P., Pont-Tuset, J., Barron, J.T., et al.: Multiscale combinatorial grouping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 328–335 (2014)Google Scholar