June 1, 2015
We explore the task of recognizing peoples’ identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset, consisting of over 60000 instances of over 2000 individuals collected from public Flickr photo albums. With only about half of the person images containing a frontal face, the recognition task is very challenging due to the large variations in pose, clothing, camera viewpoint, image resolution and illumination. We propose the Pose Invariant PErson Recognition (PIPER) method, which accumulates the cues of poselet-level person recognizers trained by deep convolutional networks to discount for the pose variations, combined with a face recognizer and a global recognizer. Experiments on three different settings confirm that in our
unconstrained setup PIPER significantly improves on the performance of
DeepFace, which is one of the best face recognizers as measured on
the LFW dataset.
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April 14, 2025
Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schoenfeld, Jonas Kohler, Markos Georgopoulos, Albert Pumarola, Ali Thabet, Artsiom Sanakoyeu
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April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
December 11, 2019
Eliya Nachmani, Lior Wolf
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April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
November 01, 2018
Yedid Hoshen, Lior Wolf
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