Face Clustering in the videos
Learning discriminative node features is the key to further improve the performance of graph-based face clustering.
Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity.
Face clustering for long-form content such as movies is challenging because of variations in appearance and lack of large-scale labeled video resources.
An effective approach to automated movie content analysis involves building a network (graph) of its characters.
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation.
In this paper, we propose a Density-Aware Feature Embedding Network (DA-Net) for the task of face clustering, which utilizes both local and non-local information, to learn a robust feature embedding.
Specifically, our analysis provides conditions that guarantee the correctness of affine subspace clustering methods both with and without the affine constraint, and shows that these conditions are satisfied for high-dimensional data.
We demonstrate our method on the challenging task of learning representations for video face clustering.
Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric constraint (FSSC), which constructs a new objective function with a symmetric constraint basing on F-norm, whose the most significant advantage is to obtain a closed-form solution of the coefficient matrix.