Discriminant Analysis for Recognition of Human Face Images (Invited Paper)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive graphical approach to entity resolution
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Learning people annotation from the web via consistency learning
Proceedings of the international workshop on Workshop on multimedia information retrieval
Web People Search via Connection Analysis
IEEE Transactions on Knowledge and Data Engineering
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Improving face clustering using social context
Proceedings of the international conference on Multimedia
Describable Visual Attributes for Face Verification and Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting Web querying for Web people search
ACM Transactions on Database Systems (TODS)
Automatic person annotation of family photo album
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Classification based group photo retrieval with bag of people features
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Query-driven approach to entity resolution
Proceedings of the VLDB Endowment
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Automatic face clustering, which aims to group faces referring to the same people together, is a key component for face tagging and image management. Standard face clustering approaches that are based on analyzing facial features can already achieve high-precision results. However, they often suffer from low recall due to the large variation of faces in pose, expression, illumination, occlusion, etc. To improve the clustering recall without reducing the high precision, we leverage the heterogeneous context information to iteratively merge the clusters referring to same entities. We first investigate the appropriate methods to utilize the context information at the cluster level, including using of "common scene", people co-occurrence, human attributes, and clothing. We then propose a unified framework that employs bootstrapping to automatically learn adaptive rules to integrate this heterogeneous contextual information, along with facial features, together. Experimental results on two personal photo collections and one real-world surveillance dataset demonstrate the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.