人脸识别使用字符串语法模糊k近邻

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-02-01 DOI:10.1109/KST.2016.7440531

Payungsak Kasemsumran, S. Auephanwiriyakul, N. Theera-Umpon

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引用次数: 14

摘要

将两种类型的隶属度值合并到字符串语法k近邻中，建立了字符串语法模糊k近邻。我们将这两个字符串语法模糊k近邻应用到人脸识别系统中。系统在ORL、MIT-CBCL、Georgia Tech、FEI和JAFFE数据库中的准确率分别为99.25%、99.75%、79.57%、93.85%和100%。尽管结果令人满意，但该系统仍存在一定的局限性。它不是尺度不变的。此外，Levenshtein距离可能会在实际上相距很远但计算距离很小的弦之间产生误解。

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Face recognition using string grammar fuzzy K-nearest neighbor

A string grammar fuzzy K-nearest neighbor is developed by incorporating 2 types of membership value into string grammar K-nearest neighbor. We apply these two string grammar fuzzy K-nearest neighbors in the face recognition system. The system provides 99.25%, 99.75%, 79.57%, 93.85%, and 100% in ORL, MIT-CBCL, Georgia Tech, FEI and JAFFE databases, respectively. Although, the results are satisfied, there are some limitations on the system. It is not scale-invariant. Also, the Levenshtein distance might create misperception between strings that are actually far apart but the calculated distance is small.

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2016 8th International Conference on Knowledge and Smart Technology (KST)

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