Person re-identification by free energy score space encoding

Person re-identification is an important and challenging computer vision problem. Recent progress in this area is due to new visual features and models that deals with cross-view variations. Instead of working towards more complex models, we focus on low level features and their encoding. Low level features capturing the color and structural information are first extracted from human images. Gaussian Mixture Model (GMM) is then employed to approximate the distribution of the features, providing a relatively comprehensive statistical representation. Finally, low level features are mapped to a space by computing free energy score of the GMM. The mapped features are encoded into a fixed-length feature vector for person re-identification. Extensive experiments are conducted on several public datasets. Comparisons with benchmark person re-identification methods show the promising performance of our approach.