  Person reidentification (re-ID) has been receiving increasing attention in
recent years due to its importance for both science and society. Machine
learning and particularly Deep Learning (DL) has become the main re-id tool
that allowed researches to achieve unprecedented accuracy levels on benchmark
datasets. However, there is a known problem of poor generalization of DL
models. That is, models trained to achieve high accuracy on one dataset perform
poorly on other ones and require re-training. To address this issue, we present
a model without trainable parameters which shows great potential for high
generalization. It combines a fully analytical feature extraction and
similarity ranking scheme with DL-based human parsing used to obtain the
initial subregion classification. We show that such combination to a high
extent eliminates the drawbacks of existing analytical methods. We use
interpretable color and texture features which have human-readable similarity
measures associated with them. To verify the proposed method we conduct
experiments on Market1501 and CUHK03 datasets achieving competitive rank-1
accuracy comparable with that of DL-models. Most importantly we show that our
method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to
transfer learning tasks. It is significantly higher than previously reported
30-50% transfer accuracy. We discuss the potential ways of adding new features
to further improve the model. We also show the advantage of interpretable
features for constructing human-generated queries from verbal description to
conduct search without a query image.
