  The overwhelming popularity of social media has resulted in bulk amounts of
personal photos being uploaded to the internet every day. Since these photos
are taken in unconstrained settings, recognizing the identities of people among
the photos remains a challenge. Studies have indicated that utilizing evidence
other than face appearance improves the performance of person recognition
systems. In this work, we aim to take advantage of additional cues obtained
from different body regions in a zooming in fashion for person recognition.
Hence, we present Zoom-RNN, a novel method based on recurrent neural networks
for combining evidence extracted from the whole body, upper body, and head
regions. Our model is evaluated on a challenging dataset, namely People In
Photo Albums (PIPA), and we demonstrate that employing our system improves the
performance of conventional fusion methods by a noticeable margin.
