Convolutional covariance features: Conception, integration and performance in person re-identification

This paper introduces a novel type of features based on covariance descriptors - the convolutional covariance features (CCF). Differently from the traditional and handcrafted way to obtain covariance descriptors, CCF is computed from adaptive and trainable features, which come from a coarse-to-fine transfer learning (CFL) strategy. CFL provides a generic-to-specific knowledge and noise-invariant information for person re-identification. After training the deep features, convolutional and flat features are extracted from, respectively, intermediate and top layers of a hybrid deep network. Intermediate layer features are then wrapped in covariance matrices, composing the so-called CCF, which are integrated to the top layer features, called here flat features. Integration of CCF and flat features demonstrated to improve the proposed person re-identification in comparison with the use of the component features alone. Our person re-identification method achieved the best top 1 performance, when compared with other 18 state-of-the-art methods over VIPeR, i-LIDS, CUHK01 and CUHK03 data sets. The compared methods are based on deep learning, covariance descriptors, or handcrafted features and similarity functions.