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摘要: 步态识别作为生物特征识别中的一种，具有远距离、非接触和难以模仿等优点.其中视角或行走方向的变化使提取的人体轮廓产生巨大差异，是影响步态识别系统性能的最主要因素之一.本文首先介绍了现有的多角度步态数据库，然后根据特征提取方式的不同，将当前已提出的方法分为三维模型法、视角不变性特征法、映射投影法和深度神经网络法四类，并详细阐述了每一类的原理、特点以及优缺点.最后，结合实际应用指出当前研究的局限性与发展趋势.Abstract: As one of biometric identification methods, gait recognition has many advantages. It is suitable for human identification at a long distance, requiring no contact and hard to imitate. One of the most important factors that affect the performance of gait recognition is that the change of view or the direction of the walk, which makes the human silhouette have a large variation. We first introduce the existing multi-view gait database. Then according to the characteristics of different extraction methods, the current approaches are divided into four kinds:three-dimensional model, view-invariant features, subspace projection as well as deep neural network, and analyses are conducted on their principle, advantages and disadvantages. Finally the limitations of current research and development trend are pointed out.
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Key words:
- Gait recognition /
- multi-view /
- behavioral characteristics recognition /
- biometrics
1) 本文责任编委 王亮 -
表 1 多视角步态库
Table 1 Databases for multiview gait
库名 建立机构 样本容量 具体角度 示例 USF[3] 南佛罗里达大学 122人, 1 870序列(地面、鞋子、负重和时间) 在摄像机前(左右两个)绕椭圆路线行走 
CASIA-A[13] 中国科学院自动化研究所 20人×3视角× 4序列= 240 3个(侧面的3个点) 
CASIA-B[14] 中国科学院自动化研究所 124人× 11视角× (8正常+2背包+ 2外套) = 13 640 11个($180^{\circ}$每间隔$18^{\circ}$度一个视角) 
HID-UMD[15] 马里兰大学 1: 25人× 4个视角= 100 正面(走向、走出)、侧面(向左、向右) 
2: 55人× 4 (2视角) = 220 T形路径(正面、侧面) 
CMU MoBo[16] 卡耐基梅隆大学 25人× 6视角× (3速度+1抱球+ 1上坡) = 600 6个($360^{\circ}$圆周每$60^{\circ}$一个视角) 
OU-ISIR Treadmill[17] 大阪大学 168人 25个(12个方位角× 2个倾斜角度+1个俯视) 
OU-ISIR LP[18] 大阪大学 4 007人 8个(2摄像机$\times $ 4个侧面角度) 
SZU RBG-D[19] 深圳大学 99人× 2视角× 4序列= 792 $90^{\circ}$和$30^{\circ} \sim 60^{\circ}$之间的一个角度 
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表 2 类能量图构造方法与性能分析
Table 2 The construction methods and performances analysis of class energy image
表 3 CASIA-B数据集上现有步态识别方法的准确率对比
Table 3 Recognition accuracy of existing approaches on CASIA-B datasets
方法 识别率(%) $54^{\circ}$ $72^{\circ}$ $90^{\circ}$ $108^{\circ}$ $126^{\circ}$ 3D模型局部能量图投影[31] 86.1 98.1 - 92.3 90.4 下肢姿态重构[47] 74.4 72.6 86.5 69.2 67.4 聚类AGI +SRML[48] 68.3 92.7 - 93.4 70.3 聚类EDI +IT2FKNN[49] 72.2 94.8 - 95.3 74.9 GEI+CCA[53] 52.0 94.6 - 93.8 78.3 C3A[55] 86.6 98.7 - 95.4 86.0 GEI+TSVD[59] 53.7 81.8 - 86.3 45.7 低秩GEI+SVD[61] 42.5 86.2 - 88.3 49.8 JSL[68] 71.4 91.7 - 90.5 73.6 KCDML[75] 76.7 94.3 - 88.1 88.4 GEI+SPAE[88] 82.3 94.4 96.0 96.0 86.3
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表 4 现有多视角步态识别方法
Table 4 Existing approaches for multiview gait recognition
模板 方法 实现途径 优点 缺点 三维模板 建立三维步态模型 利用多摄像机对人体结构或人体运动进行3D建模[22, 27-35] 更准确地表达人体各个部位的物理空间; 能够降低遮挡等因素的负面影响. 需要全可控的多摄像机协作的识别环境; 摄像机平衡视角和建模计算复杂. 提取视角不变性特征 提取局部特征[44-47]; 聚类图像估计视角[48, 49]; 其他[38, 51]. 直接提取不随着角度而改变的步态特征进行身份识别, 避免轮廓差异; 思路直观, 计算简单, 易于理解和实现. 仅仅适用于视角变化有限的情况下; 易受到遮挡因素或服饰变化的破坏. 二维图像或视频序列特征 学习不同视角下的映射或投影关系 典范相关分析(CCA)[53-57]; 视角转换模型(VTM)[58-65]; 其他(LDA、CML、MPCA与核扩展等)[66-77]. 投影到子空间中获得步态的视角不变特征, 减小同一行人不同视角下的类内方差; 具有相对较高的识别精度. 步态图像转换成向量后维数常高达上万维, 计算量很大; 视角变化较大时效果不理想; CCA和CML类的方法仅能利用两个视角间的互补信息, 处理N个视角时要重复N次来学习N对特征映射; VTM方法在进行模型构建和视角转化时容易造成噪声传播, 致使识别性能退化. 基于深度神经网络 CNN[80-86]与AutoEncoder[88] 无图像的预处理过程; 有效提取步态特征; 具有相对较高的识别精度. 需要大量数据用于训练; 卷积神经网络缺乏对时间序列信号的记忆功能; 基于自动编码的VTM也有在转化时容易造成噪声传播使识别性能退化的问题.
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