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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition
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Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition

机译：开发强大的多尺度特色局部二进制模式用于改进的面部表情识别

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Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.
机译：引人注目的面部表情识别（FER）流程已经在非常成功的电脑视觉，机器人，人工智能和动态纹理识别等领域中使用。然而，传统局部二进制模式（LBP）的FER的关键问题是与可以影响面部图像纹理的不同比例相关的相邻像素的丢失。为了克服这些限制，该研究描述了一种新的扩展LBP方法，用于从图像中提取特征向量，从面部表达中检测每个图像。所提出的方法基于应用于LBP（8,1）和LBP（8,2）上的两个旋转内核的按位和操作，并利用两个可访问的数据集。首先，检测面部部件，观察面部的基本部件，例如眼睛，鼻子和嘴唇。然后裁剪面的面部以减小尺寸，并且施加遮蔽内核以锐化图像。滤波图像然后通过特征提取方法并等待分类过程。四种机器学习分类器用于验证所提出的方法。本研究表明，所提出的多级特色本地二进制图案（MSFLBP）以及支持向量机（SVM），优于最近的基于LBP的最新方法，使得延长的精度为99.12％ Cohn-Kanade（CK +）数据集和Karolinska定向情感面（KDEF）数据集的89.08％。

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