Abstract
Recent years, image-based 2D face recognition has achieved human-level performance with the big breakthrough of deep learning paradigm. However, almost all of the existing deep face recognition methods depend on millions and millions of labeled 2D face images from different individual for supervised deep learning. In this case, face labelling becomes the pain point of deep face recognition. To solve this issue, we propose a novel clustering driven unsupervised deep face recognition framework, namely ClusterFace. In particular, our framework firstly assume that we already have a well-trained deep face model and a large number of face images without any labels. Then, all these face images are represented by this deep face model and then unsupervised clustered into different clusters using a certain clustering algorithm. Finally, these clustering-based face labelling results are employed to train a new deep CNN model for face recognition. Experimental results demonstrated that the proposed framework with a simple Mini-batch K-Means clustering algorithm can achieve surprising state-of-the-art performance (99.41%) on the LFW dataset. We also presented an intuitional explanation the reason of achieving good performance of our framework and also demonstrated its robustness to the choice of the number of clusters and the amount of unlabeled face images.
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Xie, L., Yu, C., Li, H., Zhu, J. (2018). ClusterFace: Clustering-Driven Deep Face Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_41
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