View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition

Abstract

Cross-view gait recognition authenticates a person using a pair of gait image sequences with different observation views. View difference causes degradation of gait recognition accuracy, and so several solutions have been proposed to suppress this degradation. One useful solution is to apply a view transformation model (VTM) that encodes a joint subspace of multiview gait features trained with auxiliary data from multiple training subjects, who are different from test subjects (recognition targets). In the VTM framework, a gait feature with a destination view is generated from that with a source view by estimating a vector on the trained joint subspace, and gait features with the same destination view are compared for recognition. Although this framework improves recognition accuracy as a whole, the fit of the VTM depends on a given gait feature pair, and causes an inhomogeneously biased dissimilarity score. Because it is well known that normalization of such inhomogeneously biased scores improves recognition accuracy in general, we therefore propose a VTM incorporating a score normalization framework with quality measures that encode the degree of the bias. From a pair of gait features, we calculate two quality measures, and use them to calculate the posterior probability that both gait features originate from the same subjects together with the biased dissimilarity score. The proposed method was evaluated against two gait datasets, a large population gait dataset of over-ground walking (course dataset) and a treadmill gait dataset. The experimental results show that incorporating the quality measures contributes to accuracy improvement in many cross-view settings.

DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

“…Following the protocol of [21,23,24] (publicly available at http://www.am.sanken.osaka-u.ac.jp/BiometricDB/dataset/GaitLP/Benchmarks.html), five 2-fold cross validations were performed. During each training phase, 956 × (956-1) = 912,980 intra-class samples and 956 × 1 = 956 inter-class samples were used for training Joint Bayesian.…”

“…For the first setting, all the subjects were used to evaluate the performance of our proposed DeepGait, so that the result could be reliable in a statistical manner. For the second setting, we used a subset of the OULP dataset following the protocol of [21,23,24] for comparison. For further comparison, experimental results, learning models, and test codes are released in Supplementary Materials.…”

“…The proposed method is further compared with other state-of-the-art methods [21,23,24] in cross-view gait recognition. Muramatsu et al [23,24] proposed the evaluation criteria and five 2-fold cross validations were performed to reduce the effect of random grouping in their experiments.…”

“…When dealing with view change problems, several appearance-based approaches are proposed: (1) the view transformation model (VTM) [23,24]; (2) the view-invariant feature-based approaches [21,25]; and (3) multiview gallery-based approaches [26,27]. On the OULP dataset, VTM-based methods are widely used: [24] proposed a generative approach which is a kind of VTM-based methods and makes use of transformation consistency measures (TCM+); [23] further proposed a quality-dependent VTM (wQVTM).…”

“…On the OULP dataset, VTM-based methods are widely used: [24] proposed a generative approach which is a kind of VTM-based methods and makes use of transformation consistency measures (TCM+); [23] further proposed a quality-dependent VTM (wQVTM). Recently, a view-invariant feature-based approach (GEINet) [21] was proposed and achieved the best performance.…”