Proceedings ArticleDOI
Aiding face recognition with social context association rule based re-ranking
TLDR
The results show that association rules extracted from social context can be used to augment face recognition and improve the identification performance.Abstract:
Humans are very efficient at recognizing familiar face images even in challenging conditions. One reason for such capabilities is the ability to understand social context between individuals. Sometimes the identity of the person in a photo can be inferred based on the identity of other persons in the same photo, when some social context between them is known. This research presents an algorithm to utilize cooccurrence of individuals as the social context to improve face recognition. Association rule mining is utilized to infer multi-level social context among subjects from a large repository of social transactions. The results are demonstrated on the G-album and on the SN-collection pertaining to 4675 identities prepared by the authors from a social networking website. The results show that association rules extracted from social context can be used to augment face recognition and improve the identification performance.read more
Citations
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A comprehensive overview of biometric fusion
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Hierarchical Representation Learning for Kinship Verification
TL;DR: In this paper, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner, and a compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is employed to verify the kin accurately.
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KVQA: Knowledge-Aware Visual Question Answering
TL;DR: KVQA is introduced – the first dataset for the task of (world) knowledge-aware VQA and is the largest dataset for exploring V QA over large Knowledge Graphs (KG), which consists of 183K question-answer pairs involving more than 18K named entities and 24K images.
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A Comprehensive Overview of Biometric Fusion
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Social Behavioral Information Fusion in Multimodal Biometrics
TL;DR: A novel person recognition approach is presented, that relies on the knowledge of individuals’ social behavior to enhance the performance of a traditional biometric system.
References
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Journal ArticleDOI
Exploring album structure for face recognition in online social networks
TL;DR: An album-oriented face-recognition model that exploits the album structure for face recognition in online social networks is proposed that is broadly applicable and can significantly improve recognition rates.
Proceedings ArticleDOI
Propagation of facial identities in a social network
TL;DR: The result demonstrates that the constraints imposed by the social network have the potential to improve the performance of face recognition methods and shows it is possible to discover hidden connections in a social network based on face recognition.
Context Networks for Annotating Personal Media
TL;DR: The results show that the availability of event context, and its dynamic discovery, can produce search spaces with almost all correct solutions, and the sizes of these search spaces were substantially smaller than the ones produced by approaches using a fixed set of sources.