A Genetic Algorithm-based 3D feature selection for lip reading

S. Morade, S. Patnaik
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引用次数: 1

Abstract

In lip reading, selection of features play crucial role. In lip reading applications database is video, so 3 Dimensional transformation is appropriate to extract lip motion information. State of art the lip reading is based on frame normalization and frame wise feature extraction. However this is not appropriate due to chances of information loss during frame normalization. Also all the frames cannot be considered equally as they bear varying motion information. In this paper 3D transform based method is proposed for feature extraction. These features are the input to Genetic Algorithm (GA) model for discriminative analysis. Genetic Algorithm is used for dimensionality reduction and to improve the performance of the classifiers at low cost of computation. Both testing and training time for classifier is reduced by compact feature size. For experimentation of digit utterances CUAVE and Tulips database are used. The results obtained are compared with various feature selectors from WEKA software. It is found that from classification accuracy point of view proposed method is better than others.
基于遗传算法的唇读三维特征选择
在唇读中,特征的选择起着至关重要的作用。在唇读应用中,数据库是视频的,因此三维变换是提取唇动信息的合适方法。目前的唇读技术是基于帧归一化和帧特征提取。然而,由于在帧规范化过程中信息丢失的可能性,这是不合适的。此外,所有的帧不能被认为是平等的,因为他们承担不同的运动信息。本文提出了一种基于三维变换的特征提取方法。这些特征是遗传算法(GA)模型判别分析的输入。遗传算法用于降维,以低计算成本提高分类器的性能。通过压缩特征大小,减少了分类器的测试和训练时间。对于数字话语的实验,使用了CUAVE和Tulips数据库。所得结果与WEKA软件中的各种特征选择器进行了比较。从分类精度的角度来看,该方法优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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