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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Gait-Based Gender Classification Using Mixed Conditional Random Field
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Gait-Based Gender Classification Using Mixed Conditional Random Field

机译：使用混合条件随机场的基于步态的性别分类

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摘要

This paper proposes a supervised modeling approach for gait-based gender classification. Different from traditional temporal modeling methods, male and female gait traits are competitively learned by the addition of gender labels. Shape appearance and temporal dynamics of both genders are integrated into a sequential model called mixed conditional random field (CRF) (MCRF), which provides an open framework applicable to various spatiotemporal features. In this paper, for the spatial part, pyramids of fitting coefficients are used to generate the gait shape descriptors; for the temporal part, neighborhood-preserving embeddings are clustered to allocate the stance indexes over gait cycles. During these processes, we employ evaluation functions like the partition index and Xie and Beni's index to improve the feature sparseness. By fusion of shape descriptors and stance indexes, the MCRF is constructed in coordination with intra- and intergender temporary Markov properties. Analogous to the maximum likelihood decision used in hidden Markov models (HMMs), several classification strategies on the MCRF are discussed. We use CASIA (Data set B) and IRIP Gait Databases for the experiments. The results show the superior performance of the MCRF over HMMs and separately trained CRFs.
机译：本文提出了一种基于步态的性别分类的监督建模方法。与传统的时间建模方法不同，通过添加性别标签可以竞争性地学习男性和女性的步态特征。性别的外观和时间动态都集成到称为混合条件随机场（CRF）（MCRF）的顺序模型中，该模型提供了适用于各种时空特征的开放框架。在本文中，对于空间部分，使用拟合系数金字塔来生成步态形状描述子。对于时间部分，将保留邻域的聚类聚类以在步态周期上分配姿态指数。在这些过程中，我们采用了诸如分区索引以及Xie和Beni的索引之类的评估功能来改善特征稀疏性。通过融合形状描述符和姿态指数，构造了MCRF，并与性别内和性别间的暂时性马尔可夫性质进行协调。与隐马尔可夫模型（HMM）中使用的最大似然决策相似，讨论了MCRF上的几种分类策略。我们使用CASIA（数据集B）和IRIP步态数据库进行实验。结果表明，MCRF优于HMM和单独训练的CRF。

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