Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Modern Information Retrieval
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Learning block importance models for web pages
Proceedings of the 13th international conference on World Wide Web
A Graph Based Approach for Naming Faces in News Photos
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
Naming faces in broadcast news video by image google
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Unsupervised Face Annotation by Mining the Web
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Identifying primary content from web pages and its application to web search ranking
Proceedings of the 20th international conference companion on World wide web
Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Labeling human faces in images contained in Web media stories enables enriching the user experience offered by media sites. We propose a lightweight framework for automatic image annotation that exploits named entities mentioned in the article to significantly boost the accuracy of face recognition. While previous works in the area labor to train comprehensive offline visual models for a pre-defined universe of candidates, our approach models the people mentioned in a given story on the y, using a standard Web image search engine as an image sampling mechanism. We overcome multiple sources of noise introduced by this ad-hoc process, to build a fast and robust end-to-end system from off-the-shelf error-prone text analysis and machine vision components. In experiments conducted on approximately 900 faces depicted in 500 stories from a major celebrity news website, we were able to correctly label 81.5% of the faces while mislabeling 14.8% of them.