Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Face Identification and Clustering   In this thesis, we study two problems based on clustering algorithms. In the
first problem, we study the role of visual attributes using an agglomerative
clustering algorithm to whittle down the search area where the number of
classes is high to improve the performance of clustering. We observe that as we
add more attributes, the clustering performance increases overall. In the
second problem, we study the role of clustering in aggregating templates in a
1:N open set protocol using multi-shot video as a probe. We observe that by
increasing the number of clusters, the performance increases with respect to
the baseline and reaches a peak, after which increasing the number of clusters
causes the performance to degrade. Experiments are conducted using recently
introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition
datasets.

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The categories of this paper are: adap-org
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: econ
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th