Person Re-identification with Data-Driven Features

Human-specified appearance features are widely used for person re-identification at present, such as color and texture histograms. Often, these features are limited by the subjective appearance of pedestrians. This paper presents a new representation to re-identification that incorporates data-driven features to improve the reliability and robustness in person matching. Firstly, we utilize a deep learning network, namely PCA Network, to learn data-driven features from person images. The features mine more discriminative cues from pedestrian data and compensate the drawback of human-specified features. Then the data-driven features and common human-specified features are combined to produce a final representation of each image. The so-obtained enriched Data-driven Representation (eDR) has been validated through experiments on two person re-identification datasets, demonstrating that the proposed representation is effective for person matching. That is, the data-driven features facilitate more accurate re-identification when they are fused together with the human-specified features.