基于切向角特征的统计步态识别

提出一种基于切向角特征的统计步态识别算法。首先利用Procrustes统计形状分析将步态序列中人体侧影轮廓的连续姿态变化表示成一个紧致的Procrustes均值形状(Procrustes Mean Shape， PMS)，作为原始步态特征。然后计算PMS上各采样点处的切矢量所对应的切向角。切向角反映轮廓形状在该点处的走向和趋势，提供一种局部的可鉴别的步态特征，称为切向角特征(Tangent Angle Feature， TAF)。最后，利用切向角度差距离来计算任意两个TAF之间的相似性，并用最简单的标准分类器实现步态识别。在CASIA数据库和SOTON数据库上的实验结果表明，该算法简单有效，得到的正确识别率优于其他现有算法。

Statistical Gait Recognition Based on Tangent Angle Features

A statistical gait recognition algorithm based on tangent angle features is proposed in this paper. Firstly, the method of Procrustes shape analysis is used to produce Procrustes compact Mean Shape (PMS) from the continuous posture variation of human body profile outline in gait sequence. The PMS is utilized as the primitive gait feature in this paper. Then the tangent angle corresponding to the tangential vector at each sample point on the PMS is computed. The tangent angle is considered to reflect the local appearance and tendency at that particular point of the outline and is treated as a local discriminative gait feature called Tangent Angle Feature (TAF). Finally, the Local Tangent angle Dissimilarity is used to measure the distance between two different TAFs, and the simplest standard classifiers are used to implement gait recognition. The experimental results on CASIA database and SOTON database show that the proposed algorithm is simple and effective and outperforms other existing approaches in terms of recognition accuracy.