提出了一种有效的基于步态能量图像的身份识别方法.首先生成合成步态能量图像(GEI)丰富训练集样本数量.然后利用在以前文献中被忽略的具有良好识别性能的Gabor相位信息作为身份特征,并采用流型学习算法保局影射(LPP)将此高维数据在低维空间表示.通过使用简单的分类策略在USF步态数据库上进行对比实验,结果表明本方法的正确识别率优于现有其他的自动步态识别算法.

This paper describes an effective gait recognition approach based on Gait Energy Image (GEI) representation.Synthetic GEI samples are first created to address the problem of lacking training data.Then the Gabor phase spectrum of GEI which was ignored in the previous works is utilized as the input feature,and it is subsequently projected into a low-dimensional space by using manifold learning algorithm Locality Preserving Projection.The proposed method is tested on the USF HumanID Database.The results show that our approach outperforms other state of the art automatic algorithms in terms of recognition accuracy.