Integrating appearance features and soft biometrics for person re-identification

Matching people in different camera views, commonly referred to as person re-identification, is an inherently challenging task due to the appearance disparity caused by view change.Other factors such as low image resolution and occlusion further compound this problem. As a highly demanded technique, person re-identification has been actively studied in recent years. Most of the existing approaches either focus on feature design or distance metric learning, based on appearance features. However, due to the view change, the appearance features may significantly vary for the same subject, resulting in matching difficulties. Instead of using features from a single modality, i.e, appearance, we propose to use multimodal features to improve the re-identification accuracy. Specifically, in this work, we leverage both appearance features and soft biometrics, i.e, human characteristics such as gender, to match individuals across cameras. We build multiple graphs, each of which represent one feature modality, and the graphs are then combined and optimized to derive the similarities between a probe and the gallery subjects. The proposed method is evaluated on the VIPeR dataset with annotated soft biometric labels. The results suggest that using multimodal features, e.g, appearance and soft biometrics, can improve the matching accuracy as compared to using appearance features only, and superior performance is obtained as compared to other state-of-the-art approaches.