A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods because these methods can learn only one projection matrix, and thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the coupled bilinear discriminant projection (CBDP). Specifically, the CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs’ spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, the CBDP can learn the optimal matrix subspace where the GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, the CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of the CBDP are both monotonically increasing, so the convergence of the CBDP is demonstrated. In the terms of accuracy, the comparative experiments on the CASIA (B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.

中文翻译：

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用于交叉视野步态识别的耦合双线性判别投影

阻碍一般步态识别系统良好性能的一个问题是步态的外观特征更容易受视图而不是身份的影响，尤其是当探测步态的行走方向与注册步态不同时。传统的投影学习方法无法解决这个问题，因为这些方法只能学习一个投影矩阵，因此对于同一主题，它不能将跨视图步态特征转换为相似的特征。本文提出了一种通过使用耦合双线性判别投影 (CBDP) 对齐跨视图的步态能量图像 (GEI) 来克服此问题的创新方法。具体来说，CBDP 使用两组双线性变换矩阵为两个视图生成对齐的步态矩阵特征，从而保留原始GEI的空间结构信息。通过迭代最大化类间距离度量与类内距离度量的比率，CBDP 可以学习最佳矩阵子空间，其中跨视图的 GEI 在水平和垂直坐标中对齐。因此，CBDP 也能够避免欠样本问题。我们还从理论上证明了 CBDP 的目标函数序列的上下界都是单调递增的，因此证明了 CBDP 的收敛性。在准确性方面，CASIA (B) 和 OU-ISIR 步态数据库的对比实验表明，我们的方法优于最先进的交叉视图步态识别方法。更令人印象深刻的是，