GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints

Abstract

Remote person identification by gait is one of the most important topics in the field of computer vision and pattern recognition. However, gait recognition suffers severely from the appearance variance caused by the view change. It is very common that gait recognition has a high performance when the view is fixed but the performance will have a sharp decrease when the view variance becomes significant. Existing approaches have tried all kinds of strategies like tensor analysis or view transform models to slow down the trend of performance decrease but still have potential for further improvement. In this paper, a discriminative projection with list-wise constraints (DPLC) is proposed to deal with view variance in cross-view gait recognition, which has been further refined by introducing a rectification term to automatically capture the principal discriminative information. The DPLC with rectification (DPLCR) embeds list-wise relative similarity measurement among intraclass and inner-class individuals, which can learn a more discriminative and robust projection. Based on the original DPLCR, we have introduced the kernel trick to exploit nonlinear cross-view correlations and extended DPLCR to deal with the problem of multiview gait recognition. Moreover, a simple yet efficient gait representation, namely gait individuality image (GII), based on gait energy image is proposed, which could better capture the discriminative information for cross view gait recognition. Experiments have been conducted in the CASIA-B database and the experimental results demonstrate the outstanding performance of both the DPLCR framework and the new GII representation. It is shown that the DPLCR-based cross-view gait recognition has outperformed the-state-of-the-art approaches in almost all cases under large view variance. The combination of the GII representation and the DPLCR has further enhanced the performance to be a new benchmark for cross-view gait recognition.

A view transformation model based on sparse and redundant representation for human gait recognition

“…Schemes following the first approach[ 14 16 34 35 36 37 38 39 ] construct a 3-D model using cooperative multiple cameras or camera calibration and then project the obtained 3-D gallery into a 2-D silhouette. In theory, 2-D gaits for any desired view can be obtained from the 3-D model, yet there are some practical limitations to this.…”

“…We follow these choices in testing the performance of the proposed method. We then use the obtained results to compare the performance of the proposed scheme with the algorithm of Zhaoxiang Zhang et al [ 39 ] and all the algorithm compared there-in. It is worth to mention that the baseline method as explained in Yu et al [ 60 ] is a simple method that does not do any action to mitigate the view angle challenge.…”

On view‐invariant gait recognition: a feature selection solution

“…Within this context, Hu et al [18] propose a novel unitary liner projection method named ViDP, which enables cross-view gait recognition to be conducted without knowing the query view angle. The recent work by Zhang et al [19] proposes a list-wise constrained discriminative projection framework on a novel gait representation to tackle the view angle variance. Apart from reporting results for cross-view matching, they also report results for the multi-view case, which outperforms other conventional subspace learning methods.…”

“…Table 12 tabulates the recognition accuracy for the case of a gallery view angle of 54 ∘ and probe data with view angles of {108 ∘ , 144 ∘ }, {90 ∘ , 162 ∘ } and {72 ∘ , 180 ∘ }. In these tables, Zhang et al (1) refers to the feature level fusion adopted by [19], Zhang et al (2) refers to the scorelevel fusion from the same work, and Zhang et al (3) refers to their multi-view DPLCR (DPLCR is the acronym of discriminative projection with list-wise constraints with rectification, which is the framework proposed by Zhang et al in their paper [30]. In their paper its performance reported on the mainstream datasets is thestate-of-the-art.).…”

“…In these tables, Zhang et al . (1) refers to the feature level fusion adopted by [19], Zhang et al . (2) refers to the score‐level fusion from the same work, and Zhang et al .…”