基于联合多特征字典稀疏表示的步态识别算法

Abstract

现有的步态识别算法多采用模型特征或整体特征进行单一特征提取，在多视角等实际情况中算法鲁棒性较差、识别率较低。针对这一问题，本文提出了一种基于联合多特征字典稀疏表示的步态识别算法。该算法选择三种不同粒度的特征：均值形状PMS、步态能量图GEI与自建特征-区域面积序列RAS，构建特征训练字典并对特征样本进行多任务联合稀疏表示，最后通过计算最小累计残差得到测试样本类别，实现特征层融合。实验结果表明，相比单一特征提取与识别，所采用的多特征联合识别方法识别率更高，且在多视角下具有一定鲁棒性，实现了特征之间的信息互补。 Most of the existing gait recognition algorithms extract the single feature using model features or global features. However, these algorithms usually have a poor robustness and a low recognition rate in practical situations such as multi-angle. To solve this problem, a gait recognition algorithm based on joint sparse representation of multi-feature dictionaries is proposed in this paper. In this algorithm, three characteristics in different particle size are selected: Procrustes Mean Shape, Gait Energy Image and Region Area Sequence which is structured in this article. Feature training dictionaries are constructed and a multidisciplinary sparse representation to feature samples is done. Finally, the test sample category is obtained by calculating the minimum cumulative residual and achieves the integration of feature layer. Experimental results show that the multi-feature joint recognition method used in this paper has a higher recognition rate and a certain robustness at multiple angles compared to single feature extraction and recognition. This paper basically fulfills the complementary information between features.