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Article Dans Une Revue Image and Vision Computing Année : 2014

Covariance Descriptor based on Bio-inspired Features for Person Re-identification and Face Verification

Résumé

Avoiding the use of complicated pre-processing steps such as accurate face and body part segmentation or image normalization, this paper proposes a novel face/person image representation which can properly handle background and illumination variations. Denoted as gBiCov, this representation relies on the combination of Biologically Inspired Features (BIF) and Covariance descriptors. More precisely, gBiCov is obtained by computing and encoding the difference between BIF features at different scales. The distance between two persons can then be efficiently measured by computing the Euclidean distance of their signatures, avoiding some time consuming operations in Riemannian manifold required by the use of Covariance descriptors. In addition, the recently proposed KISSME framework is adopted to learn a metric adapted to the representation. To show the effectiveness of gBiCov, experiments are conducted on three person re-identification tasks (VIPeR, i-LIDS and ETHZ) and one face verification task (LFW), on which competitive results are obtained. As an example, the matching rate at rank 1 on the VIPeR dataset is of 31.11%, improving the best previously published result by more than 10%.
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Dates et versions

hal-01009958 , version 1 (19-06-2014)

Identifiants

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Bingpeng Ma, Yu Su, Frédéric Jurie. Covariance Descriptor based on Bio-inspired Features for Person Re-identification and Face Verification. Image and Vision Computing, 2014, 32 (6-7), pp.379-390. ⟨10.1016/j.imavis.2014.04.002⟩. ⟨hal-01009958⟩
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