  Balancing computational efficiency with recognition accuracy is one of the
major challenges in real-world video-based face recognition. A significant
design decision for any such system is whether to process and use all possible
faces detected over the video frames, or whether to select only a few "best"
faces. This paper presents a video face recognition system based on
probabilistic Multi-Region Histograms to characterise performance trade-offs
in: (i) selecting a subset of faces compared to using all faces, and (ii)
combining information from all faces via clustering. Three face selection
metrics are evaluated for choosing a subset: face detection confidence, random
subset, and sequential selection. Experiments on the recently introduced MOBIO
dataset indicate that the usage of all faces through clustering always
outperformed selecting only a subset of faces. The experiments also show that
the face selection metric based on face detection confidence generally provides
better recognition performance than random or sequential sampling. Moreover,
the optimal number of faces varies drastically across selection metric and
subsets of MOBIO. Given the trade-offs between computational effort,
recognition accuracy and robustness, it is recommended that face feature
clustering would be most advantageous in batch processing (particularly for
video-based watchlists), whereas face selection methods should be limited to
applications with significant computational restrictions.
