Face Clustering
21 papers with code • 1 benchmarks • 3 datasets
Face Clustering in the videos
Libraries
Use these libraries to find Face Clustering models and implementations
EasyCom
MovieGraphs
A View From Somewhere (AVFS)
Latest papers
Learning to Cluster Faces via Confidence and Connectivity Estimation
yl-1993/learn-to-cluster
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CVPR 2020
With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters.
Video Face Clustering with Unknown Number of Clusters
makarandtapaswi/BallClustering_ICCV2019
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ICCV 2019
Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing.
Linkage Based Face Clustering via Graph Convolution Network
yl-1993/learn-to-cluster
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CVPR 2019
The key idea is that we find the local context in the feature space around an instance (face) contains rich information about the linkage relationship between this instance and its neighbors.
An Internal Validity Index Based on Density-Involved Distance
One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters.
Self-Supervised Learning of Face Representations for Video Face Clustering
In this paper, we address video face clustering using unsupervised methods.
AN ONLINE ALGORITHM FOR CONSTRAINED FACE CLUSTERING IN VIDEOS
We address the problem of face clustering in long, real world videos. This is a challenging task because faces in such videos exhibit wid evariability in scale, pose, illumination, expressions, and may also be partially occluded.
Robust Subspace Clustering via Tighter Rank Approximation
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data
The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i. e., separating points drawn from a union of subspaces).
Robust Subspace Clustering via Smoothed Rank Approximation
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
ChongYou/subspace-clustering
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CVPR 2016
Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success.
