Learning to Match Appearances by Correlations in a Covariance Metric Space

This paper addresses the problem of appearance matching across disjoint camera views. Significant appearance changes, caused by variations in view angle, illumination and object pose, make the problem challenging. We propose to formulate the appearance matching problem as the task of learning a model that selects the most descriptive features for a specific class of objects. Learning is performed in a covariance metric space using an entropy-driven criterion. Our main idea is that different regions of the object appearance ought to be matched using various strategies to obtain a distinctive representation. The proposed technique has been successfully applied to the person re-identification problem, in which a human appearance has to be matched across non-overlapping cameras. We demonstrate that our approach improves state of the art performance in the context of pedestrian recognition.