  Person recognition methods that use multiple body regions have shown
significant improvements over traditional face-based recognition. One of the
primary challenges in full-body person recognition is the extreme variation in
pose and view point. In this work, (i) we present an approach that tackles pose
variations utilizing multiple models that are trained on specific poses, and
combined using pose-aware weights during testing. (ii) For learning a person
representation, we propose a network that jointly optimizes a single loss over
multiple body regions. (iii) Finally, we introduce new benchmarks to evaluate
person recognition in diverse scenarios and show significant improvements over
previously proposed approaches on all the benchmarks including the photo album
setting of PIPA.
