Abstract
Clustering is an important technique in data mining. It separates data
points into different groups or clusters in such a way that objects in
the same group are more similar to each other in some sense than with
the objects in other groups. Gabor face clustering using affinity
propagation and structural similarity index is composed of: A
representation based on Gabor filters which has been shown to perform
very well in face features, Affinity propagation clustering algorithm
which is flexible, high speed, and does not require to specify the
number of clusters, and structural similarity index which is a very
powerful method for measuring the similarity between two images.
Experimental results on two benchmark face datasets (LFW and IJB-B) show
that our method outperforms well known clustering algorithms such as
k-means, spectral clustering and Agglomerative