Abstract:

The key challenge of large scale face clustering is to extract effective facial features and construct an accurate model with less time complexity. In this research, effective features are first extracted based on convolutional neural network (CNN). Then K-means, a classical cluster algorithm, and a state-of-art algorithm named CFSFDP (Clustering by Fast Search and Find of Density Peaks) are used to cluster large scale face images. Rand Index, entropy, F₁-measure and the visualization of confusion matrix are further applied to comprehensively assess clustering quality. All the tests are under the condition of the increasing numbers of clustering centers. Experiment results demonstrate that K-means has a better performance as well as a much higher speed than CFSFDP. This conclusion is believed to shed new light in the area of face clustering.

Key words: large scale face clustering; convolutional neural network; Kmeans; rand index(RI); entropy; F₁-measure; visualization of confusion matrix