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We explore the task of recognizing peoples' identities in photo albums in anunconstrained setting. To facilitate this, we introduce the new People In PhotoAlbums (PIPA) dataset, consisting of over 60000 instances of 2000 individualscollected from public Flickr photo albums. With only about half of the personimages containing a frontal face, the recognition task is very challenging dueto the large variations in pose, clothing, camera viewpoint, image resolutionand illumination. We propose the Pose Invariant PErson Recognition (PIPER)method, which accumulates the cues of poselet-level person recognizers trainedby deep convolutional networks to discount for the pose variations, combinedwith a face recognizer and a global recognizer. Experiments on three differentsettings confirm that in our unconstrained setup PIPER significantly improveson the performance of DeepFace, which is one of the best face recognizers asmeasured on the LFW dataset.

Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues