Covariance descriptor based on bio-inspired features for person re-identification and face verification

Abstract

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 [1]. 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 [2] 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%.

Ensemble of different approaches for a reliable person re-identification system

“…a representation that combines biologically inspired features and covariance descriptors, called gBiCov [15], Symmetry-Driven Accumulation of Local Features (SDALF) [8], Custom Pictorial Structures (CPS) [7] based on chromatic content and color displacement (CCD), Color Invariants (CI) [12], and the Skeleton-based Person Signature (SPS) technique [17]. Moreover, we propose variants of such approaches, obtained by varying the features used for describing the images and by using different distance measures.…”

Age estimation via face images: a survey

“…Primates are known to be able to recognize visual patterns with high accuracy. Recent studies in computer vision and brain cognition show that biologically inspired models (BIM) improve face identification performance [118], object recognition [119], and scene classification [120]. Visual cortex application in age estimation tasks saw some improvement in age estimation accuracies.…”

Deep Learning for Person Reidentification Using Support Vector Machines

“…We compare our proposed approach with the following three available and typical person reidentification features: Ensemble of Local Features (ELF) [42], gBiCov [12], and HSV with Lab and LBP feature proposed in [18]. In the experiment, we used ELF6 implemented in [42].…”

“…Same as the pretrained strategy for CUHK02 dataset used on VIPeR dataset, we chose the following approaches as baselines: ELF18 [42], gBiCov [12], and Local Maximal Occurrence (LOMO) representation [33]. The ELF18 feature is the same as ELF6 which is computed from eighteen equally divided horizontal stripes histograms rather than six.…”

“…It has been indicated that three important cues for person reidentification are color information, texture descriptors, and interest points; some of these features are learned from datasets and others are designed by hand. Low-level features such as biologically inspired features (BIF) [12], color histograms and variants [13][14][15][16][17], local binary patterns (LBP) [13,14,17,18], Gabor features [14], and interest points (color SIFT [19,20] and SURF [21]) were proposed to represent appearance features of different people from nonoverlapping cameras. Some other works have also investigated combinations of multiple visual features, including [13,14,16].…”