Multi-view discriminant analysis with tensor representation and its application to cross-view gait recognition
Multi-view discriminant analysis with tensor representation and its application to cross-view gait recognition
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DOI：
10.1109/fg.2015.7163131
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发表时间：
2015-05
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影响因子：
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通讯作者：
Yasushi Makihara;Al Mansur;D. Muramatsu;Md. Zasim Uddin;Y. Yagi
中科院分区：
文献类型：
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作者：
Yasushi Makihara;Al Mansur;D. Muramatsu;Md. Zasim Uddin;Y. Yagi
本文描述了一种使用相对较少的训练样本进行跨视图识别的判别分析方法。由于识别目标的外观（例如，面部、步态、手势和动作）通常会随着观察视图的变化而发生巨大变化，因此我们引入了多个特定于视图的投影矩阵，并考虑通过相应的特定于视图的投影矩阵将识别目标从某个视图投影到公共判别子空间中。此外，原始高阶张量对象（例如步态识别中的时空图像）的传统矢量化表示经常遭受维数困境的诅咒，因此我们将多个特定于视图的投影矩阵封装在张量表示的判别分析框架中，这使我们能够克服维数困境的诅咒。使用两个公开可用的步态数据库进行的跨视图步态识别实验表明了该方法在训练样本量较小的情况下的有效性。
This paper describes a method of discriminant analysis for cross-view recognition with a relatively small number of training samples. Since appearance of a recognition target (e.g., face, gait, gesture, and action) is in general drastically changes as an observation view changes, we introduce multiple view-specific projection matrices and consider to project a recognition target from a certain view by a corresponding view-specific projection matrix into a common discriminant subspace. Moreover, conventional vectorized representation of an originally higher-order tensor object (e.g., a spatio-temporal image in gait recognition) often suffers from the curse of dimensionality dilemma, we therefore encapsulate the multiple view-specific projection matrices in a framework of discriminant analysis with tensor representation, which enables us to overcome the curse of dimensionality dilemma. Experiments of cross-view gait recognition with two publicly available gait databases show the effectiveness of the proposed method in case where a training sample size is small.