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• W565084530 abstract "The ever popularity of online social media, and the ubiquity of video cameras, provide an unique source of information that is otherwise not available for military, security, and forensics applications. As a result, robust recognition of the faces presented in these unconstrained images and videos became an emerging need. For example, both the Vancouver (Canada) Riots 2011, and the tragedy Boston Bombing 2013, called for robust facial recognition technologies to identify the suspects from low quality images and videos from unconstrained sources. In this paper, we briefly summarize our work on probabilistic elastic part model, which produces a pose invariant and compact representation for both image and video faces. The model produces a fixed dimension representation no matter how many frames a video face contains. This allows the representations produced from video faces of arbitrary frame numbers to be directly compared without the need of computationally expensive frame-to-frame matching. The probabilistic elastic part model produces state-of-the-art results in several real-world face recognition benchmarks, which we will also briefly discuss." @default.
• W565084530 created "2016-06-24" @default.
• W565084530 creator A5081114810 @default.
• W565084530 date "2015-01-01" @default.
• W565084530 modified "2023-10-07" @default.
• W565084530 title "Probabilistic Elastic Part Model for Real-World Face Recognition" @default.
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• W565084530 doi "https://doi.org/10.1007/978-3-319-13737-7_1" @default.
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