  Person Re-Identification (ReID) matches pedestrians across disjoint cameras.
Existing ReID methods adopting real-value feature descriptors have achieved
high accuracy, but they are low in efficiency due to the slow Euclidean
distance computation as well as complex quick-sort algorithms. Recently, some
works propose to yield binary encoded person descriptors which instead only
require fast Hamming distance computation and simple counting-sort algorithms.
However, the performances of such binary encoded descriptors, especially with
short code (e.g., 32 and 64 bits), are hardly satisfactory given the sparse
binary space. To strike a balance between the model accuracy and efficiency, we
propose a novel Sub-space Consistency Regularization (SCR) algorithm that can
speed up the ReID procedure by $0.25$ times than real-value features under the
same dimensions whilst maintaining a competitive accuracy, especially under
short codes. SCR transforms real-value features vector (e.g., 2048 float32)
with short binary codes (e.g., 64 bits) by first dividing real-value features
vector into $M$ sub-spaces, each with $C$ clustered centroids. Thus the
distance between two samples can be expressed as the summation of the
respective distance to the centroids, which can be sped up by offline
calculation and maintained via a look-up table. On the other side, these
real-value centroids help to achieve significantly higher accuracy than using
binary code. Lastly, we convert the distance look-up table to be integer and
apply the counting-sort algorithm to speed up the ranking stage.
  We also propose a novel consistency regularization with an iterative
framework. Experimental results on Market-1501 and DukeMTMC-reID show promising
and exciting results. Under short code, our proposed SCR enjoys
Real-value-level accuracy and Hashing-level speed.
