Abstract:
Clustering identities in a broadcast video is a useful
task to aid in video annotation and retrieval. Quality based frame
selection is a crucial task in video face clustering, to both improve
the clustering performance and reduce the computational cost.
We present a frame work that selects the highest quality frames
available in a video to cluster the face. This frame selection
technique is based on low level and high level features (face
symmetry, sharpness, contrast and brightness) to select the
highest quality facial images available in a face sequence for
clustering. We also consider the temporal distribution of the
faces to ensure that selected faces are taken at times distributed
throughout the sequence. Normalized feature scores are fused and
frames with high quality scores are used in a Local Gabor Binary
Pattern Histogram Sequence based face clustering system. We
present a news video database to evaluate the clustering system
performance. Experiments on the newly created news database
show that the proposed method selects the best quality face
images in the video sequence, resulting in improved clustering
performance.
