远距离复杂背景鲁棒的步态特征提取与表示方法

远距离复杂背景下步态图像通常受到噪声的影响很大。Gabor特征在此类步态识别中显示了良好的特性，然而一些基于Gabor特征的算法使用较多的模板从而导致计算量增大。为解决这个问题，本文提出了一种新的基于改进Gabor特征的步态特征提取与表示方法。首先突出步态能量图中的有效区域，并抑制易受噪声干扰的区域。然后构造一个同时具有两个方向互补特性的基本的滤波器，经过缩放和旋转，生成一系列滤波器。使用这些滤波器对改进的步态能量图以及步态差异图像进行卷积，得到两个特征向量集合以表示此步态对象。使用最近邻分类计算出本文方法在USF步态数据库上的识别率，与相关算法的比较证实了此步态特征提取与表示方法的有效性。对算法的计算量分析表明，本文算法所需的计算量比相关算法有较大降低。 

Robust Gait Feature Extraction and Representation Method with Complex Background at a Distance

Gait images obtained with complex background at a distance are usually much affected by noise. Gabor features show good performance in the gait recognition work under this situation, but the multiple templates used by some Gabor features based algorithms lead to increased computational complexity. To solve the problem, a new improved Gabor features based gait feature extraction and representation method is proposed in this paper. Valid regions in gait energy image are emphasized, and regions susceptible to noises are suppressed. A basic filter with complementary characteristic in two directions is constructed, and a series of filters are generated by scaling and rotating the basic filter. The filters are used to convolute with improved gait energy image and gait difference image, two feature vector sets are obtained and used to represent the gait subject. The nearest neighbor classifier is adopted to calculate recognition rates on USF database. The comparison result with relevant algorithms confirms the effectiveness of the proposed feature extraction and representation method. The analysis of computational complexity shows that, the proposed algorithm achieves less computational consumption compared with relevant algorithm.