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Single Sample Face Recognition: Discriminant Scaled Space vs Sparse Representation-Based Classification

机译:单个样本人脸识别:判别缩放空间与基于稀疏表示的分类

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Sparse Representation-based Classification (SRC) is an effective solution of face recognition as there have been many studies around it. However, classical SRC needs a large train data for the galley to produce an over-complete dictionary which result in high accuracy. This paper purposes to show that when there is only one sample per subject for the gallery, the simple linear Discriminant Scaled Space (DSS) can outperform classical SRC and is competitive with new single sample version of that along with significantly less runtime. In addition, it will be shown that SRC methods can be computed on the data projected to DSS which result in higher accuracy with less run time. To show the effectiveness of DSS, it is compared with different kinds of SRC on 11 public databases.
机译:基于稀疏表示的分类(SRC)是有效的人脸识别解决方案,因为围绕它的研究很多。但是,传统的SRC需要大量的火车数据才能使厨房生成不完整的字典,从而导致精度很高。本文旨在说明,当画廊的每个主题只有一个样本时,简单的线性判别比例空间(DSS)可以胜过经典SRC,并且与新的单个样本版本相比具有竞争优势,并且运行时间大大减少。另外,将显示可以在投影到DSS的数据上计算SRC方法,从而以更少的运行时间获得更高的准确性。为了显示DSS的有效性,在11个公共数据库上将它与不同类型的SRC进行了比较。

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