24,685 research outputs found
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
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Face detection and recognition benchmarks have shifted toward more difficult
environments. The challenge presented in this paper addresses the next step in
the direction of automatic detection and identification of people from outdoor
surveillance cameras. While face detection has shown remarkable success in
images collected from the web, surveillance cameras include more diverse
occlusions, poses, weather conditions and image blur. Although face
verification or closed-set face identification have surpassed human
capabilities on some datasets, open-set identification is much more complex as
it needs to reject both unknown identities and false accepts from the face
detector. We show that unconstrained face detection can approach high detection
rates albeit with moderate false accept rates. By contrast, open-set face
recognition is currently weak and requires much more attention.Comment: This is an ERRATA version of the paper originally presented at the
International Joint Conference on Biometrics. Due to a bug in our evaluation
code, the results of the participants changed. The final conclusion, however,
is still the sam
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