Facial Expression Recognition Using Shearlet Transform and Kirsch Masking

被引：0

作者：

Fatema-Tuz-Zohora, Kaniz ^{[1
]}

Ferdousi, Adnan ^{[1
]}

Jabid, Taskeed ^{[1
]}

机构：

[1] East West Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh

来源：

PROCEEDINGS OF THE 2019 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS (IEEE ICSIPA 2019) | 2019年

关键词：

feature extractor; facial expression recognition; local binary pattern; discrete shearlet transform; kirsch compass kernel; LOCAL-BINARY-PATTERN; CLASSIFICATION;

D O I：

10.1109/icsipa45851.2019.8977734

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new feature extractor is proposed that is combined of Discrete Shearlet Transform (DST) system and Kirsch Compass Kernel (KCK) with Local Binary Pattern (LBP) for extracting features from images. Discrete Shearlet Transform gives multiresolute images of an image by highlighting the major edges along with detail features. On the other hand, Kirsch Compass Kernel (KCK) provides 8 different directional images with prominent edges which sharpen the edges in each direction. These characteristics of DST and KCK help LBP to extract the significant features of an image. The extracted features by LBP help to classify the images by using Support Vector Machine (SVM).

引用

页码：35 / 40

页数：6

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