Nowadays, there is a growing interest in security applications using embedded smart cameras. Despite the rising attention to re-identifying the people in a non-overlapping embedded camera network, there exist no comparative evaluation of the existing schemes, specially facial representations. Though, facial features offer the advantage of remaining stable over much larger time intervals in contrast to commonly exploited features for person re-identification, such as whole body appearance. However, the challenge in using faces for such applications, apart from low captured face resolutions, is that their appearance is largely influenced by changes in illumination, pose, viewpoint, background and partial occlusions. Therefore, in this paper, we present a comparative study of facial local features for the task of person re-identification using embedded smart cameras in a realistic surveillance scenario. The experiments are performed on a novel data set which we make publicly available for future research in this area. The contributions of this work are as follows: 1) evaluation of eight baseline facial local features for person re-identification using embedded smart cameras; 2) an empirical study of the performance of person re-identification systems based on local facial features when fused with auxiliary information such as face shape, face skin color tone, hair color, and cloth color; 3) comparative study of single- and multi-shot approaches; 4) a novel data set using embedded cameras for person re-identification with properties unavailable in exiting data sets. Copyright 2014 ACM.
