Face Identification and Clustering
This work addresses incremental improvements in face recognition for security or surveillance applications, focusing on specific datasets.
The thesis tackled improving face identification and clustering by using visual attributes to narrow search areas and optimizing cluster counts in video-based protocols, showing performance increases with more attributes and a peak performance at optimal cluster numbers before degradation.
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.