In this papers, we proposed an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual.
SDALF has been extensively tested on the following datasets:
- VIPeR
- ETHZ
- CAVIAR4REID
- iLIDS provided under request: W. Zheng, S. Gong, and T. Xiang. Associating groups of people. In BMVC, 2009.
