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.

本文介绍一种融合 Fisher 向量和深度神经网络的混合结构，以学习人像的非线性表示，进而实现对行人重新识别流程的优化。经过基于四个基准数据集 (VIPeR, CUHK03, CUHK01 和 Market1501) 的广泛实验验证，该模型达到了最先进的结果。

Fisher网络上的深度线性判别分析：一种用于人员再识别的混合架构