Facing scalability: Naming faces in an online social network
Pattern Recognition
Where is who: large-scale photo retrieval by facial attributes and canvas layout
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Video-to-video face authentication system robust to pose variations
Expert Systems with Applications: An International Journal
Proceedings of the 20th ACM international conference on Multimedia
Face verification of age separated images under the influence of internal and external factors
Image and Vision Computing
Towards person identification and re-identification with attributes
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Image ranking via attribute boosted hypergraph
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
A unified framework for context assisted face clustering
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Visualizing progressive discovery
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
CueNet: a context discovery framework to tag personal photos
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Face recognition for web-scale datasets
Computer Vision and Image Understanding
Pattern Recognition Letters
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We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: They can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images. We demonstrate the current effectiveness—and explore the future potential—of using attributes for face verification and image search via human and computational experiments. Finally, we introduce two new face data sets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.