An enhanced deep feature representation for person re-identification
Shangxuan Wu, Ying-Cong Chen, Xiang Li, An-Cong Wu, Jin-Jie You, Wei-Shi Zheng
January 2016
Abstract
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.
Publication
IEEE winter conference on applications of computer vision (WACV)

Assistant Professor
Ying-Cong Chen is an Assistant Professor at AI Thrust, Information Hub of Hong Kong University of Science and Technology (Guangzhou Campus). He obtained his Ph.D. degree from the Chinese University of Hong Kong. His research lies in the broad area of computer vision and machine learning, aiming for empowering machine with the capacity to understand human appearance, physiology and psychology. His works contribute to a wide range of applications, including contactless health monitoring, semantic photo synthesis, and intelligent video surveillance.