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• W1557066151 abstract "The Sparse Classification approach for image based face recognition assumes that each test sample can be expressed as a linear combination of training samples of the correct class. We propose a video based face recognition approach based on the same assumption. Our formulation requires solving a low-rank row-sparse Multiple Measurement Vector (MMV) recovery problem. Such a row-sparse MMV matrix is low rank as well. Since low rank row sparse MMV recovery is not a well-studied problem, we propose a novel algorithm to solve such it. The experimental evaluation is carried on the VidTIMIT database. The proposed method yields better results than state-of-the-art methods in video based frontal face recognition." @default.
• W1557066151 created "2016-06-24" @default.
• W1557066151 creator A5020310463 @default.
• W1557066151 date "2015-07-01" @default.
• W1557066151 modified "2023-09-25" @default.
• W1557066151 title "Frontal face recognition from video via rank-aware multiple measurement vector recovery" @default.
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• W1557066151 doi "https://doi.org/10.1109/icdsp.2015.7252077" @default.
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