  Person re-identification is to seek a correct match for a person of interest
across views among a large number of imposters. It typically involves two
procedures of non-linear feature extractions against dramatic appearance
changes, and subsequent discriminative analysis in order to reduce intra-
personal variations while enlarging inter-personal differences. In this paper,
we introduce a hybrid architecture which combines Fisher vectors and deep
neural networks to learn non-linear representations of person images to a space
where data can be linearly separable. We reinforce a Linear Discriminant
Analysis (LDA) on top of the deep neural network such that linearly separable
latent representations can be learnt in an end-to-end fashion. By optimizing an
objective function modified from LDA, the network is enforced to produce
feature distributions which have a low variance within the same class and high
variance between classes. The objective is essentially derived from the general
LDA eigenvalue problem and allows to train the network with stochastic gradient
descent and back-propagate LDA gradients to compute the gradients involved in
Fisher vector encoding. For evaluation we test our approach on four benchmark
data sets in person re-identification (VIPeR [1], CUHK03 [2], CUHK01 [3], and
Market1501 [4]). Extensive experiments on these benchmarks show that our model
can achieve state-of-the-art results.
