Abstract: Person re-identification is an important technique for automatically searching for
pedestrians in surveillance videos. This technology consists of two key parts, feature representation
and metric learning. Effective feature representations should be robust to changes in illumination and
viewpoint, and the discriminative metric learning can improve the matching accuracy of person
images. However, most of the existing features were based on local or global feature representation
and failed to efficiently use the fine details and profile information of the appearance of pedestrians.
More importantly, metric learning was usually conducted in a linear feature space, and nonlinear
structures in the feature space couldn’t be efficiently utilized. To solve these problems, we first
designed an effective feature representation called enhanced local maximal occurrence representation
(eLOMO), which could realize the fusion of fine details and profile information of the appearance of the person image and satisfy the human visual recognition mechanism. Furthermore, we proposed a
kernelized KISSME metric learning (k-KISSME) method, simple and efficient, only requiring two
inverse covariance matrices to be estimated. In addition, to handle changes in light and viewing angle,
we applied Retinex transforms and scale-invariant texture descriptors. Experiments show that the
proposed method possesses the ability regarding abundant and integral person feature representation
and improves the recognition rate of person re-identification in comparison with the existing
mainstream methods.

Key words: person re-identification, enhanced local maximal occurrence feature, kernel-based learning, feature representation, metric learning