  Feature representation and metric learning are two critical components in
person re-identification models. In this paper, we focus on the feature
representation and claim that hand-crafted histogram features can be
complementary to Convolutional Neural Network (CNN) features. We propose a
novel feature extraction model called Feature Fusion Net (FFN) for pedestrian
image representation. In FFN, back propagation makes CNN features constrained
by the handcrafted features. Utilizing color histogram features (RGB, HSV,
YCbCr, Lab and YIQ) and texture features (multi-scale and multi-orientation
Gabor features), we get a new deep feature representation that is more
discriminative and compact. Experiments on three challenging datasets (VIPeR,
CUHK01, PRID450s) validates the effectiveness of our proposal.
