Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Affine Invariant Clustering and Automatic Cast Listing in Movies
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Name-It: Association of Face and Name in Video
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Navigating massive data sets via local clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Naming every individual in news video monologues
Proceedings of the 12th annual ACM international conference on Multimedia
Fast Approximate Similarity Search in Extremely High-Dimensional Data Sets
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multiple instance learning for labeling faces in broadcasting news video
Proceedings of the 13th annual ACM international conference on Multimedia
Joint manifold distance: a new approach to appearance based clustering
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Human faces play an important role in efficiently indexing and accessing video contents, especially broadcasting news video. However, face appearance in real environments exhibits many variations such as pose changes, facial expressions, aging, illumination changes, low resolution and occlusion, making it difficult for current state of the art face recognition techniques to obtain reasonable retrieval results. To handle this problem, this paper proposes an efficient retrieval method by integrating temporal information into facial intensity information. First, representative faces are quickly generated by using facial intensities to organize the face dataset into clusters. Next, temporal information is introduced to reorganize cluster memberships so as to improve overall retrieval performance. For scalability and efficiency, the clustering is based on a recently-proposed model involving correlations among relevant sets (neighborhoods) of data items. Neighborhood queries are handled using an approximate search index. Experiments on the 2005 TRECVID dataset show promising results.