融合局部紋理和形狀特征的人臉表情識別

胡敏; 滕文娣; 王曉華; 許良鳳; 楊娟

doi:10.11999/JEIT170799

融合局部紋理和形狀特征的人臉表情識別

doi: 10.11999/JEIT170799

2.
(合肥工業(yè)大學(xué)計算機(jī)與信息學(xué)院合肥 230009) ②(情感計算與先進(jìn)智能機(jī)器安徽省重點實驗室合肥 230009)

基金項目:

國家自然科學(xué)基金項目(61672202, 61432004, 61502141)，國家自然科學(xué)基金-深圳聯(lián)合基金重點項目(U1613217)，安徽高校省級自然科學(xué)研究重點項目(KJ2017A368)

計量
- 文章訪問數(shù): 1612
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- PDF下載量: 314
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-08-07
- 修回日期: 2017-12-28
- 刊出日期: 2018-06-19

Facial Expression Recognition Based on Local Texture and Shape Features

2.
(School of Computer and Information of Hefei University of Technology, Hefei 230009, China)

Funds:

The National Natural Science Foundation of China (61672202, 61432004, 61502141), The National Natural Science Foundation of China-Shenzhen Joint Foundation (Key Project) (U1613217), The Key University Science Research Project of Anhui Province (KJ2017A368)

摘要

摘要: 針對局部二值模式(LBP)、中心對稱局部二值模式(CS-LBP)和梯度方向直方圖(HOG)的不足進(jìn)行改進(jìn)，該文提出中心對稱局部平滑二值模式(CS-LSBP)和絕對梯度方向直方圖(HOAG)，并提出一種融合局部紋理特征和局部形狀特征的人臉表情識別方法。該方法首先采用CS-LSBP算子和HOAG算子分別提取人臉表情圖像的局部紋理特征和局部形狀特征，然后使用典型線性分析法(CCA)進(jìn)行特征融合，最后利用支持向量機(jī)(SVM)進(jìn)行表情分類。在JAFFE人臉表情庫和Cohn-Kanade(CK)人臉表情庫上的實驗結(jié)果表明，改進(jìn)的特征提取方法能更加完整、精確地提取圖像的細(xì)節(jié)信息，基于CCA的特征融合方法能充分發(fā)揮特征的表征能力，該文所提人臉表情識別方法取得了較好的分類識別效果。
- 人臉表情識別 /
- 中心對稱局部平滑二值模式 /
- 絕對梯度方向直方圖 /
- 典型相關(guān)分析
Abstract: In order to improve the inadequacies of Local Binary Pattern (LBP), Center-Symmetric Local Binary Pattern (CS-LBP) and Histogram of Oriented Gradient (HOG) algorithm, Center-Symmetric Local Smooth Binary Pattern (CS-LSBP) and Histogram of Oriented Absolute Gradient (HOAG) are proposed, and a facial expression recognition method based on local texture and local shape features is proposed in this paper. Firstly, CS-LSBP and HOAG are used to extract two local features of expression image of the face. Then, Canonical Correlation Analysis (CCA) is used to fuse two local features. Finally, Support Vector Machine (SVM) is performed for the expression classification. Experimental results on JAFFE and Cohn-Kanade (CK) facial expression databases show that, the improved feature extraction method can extract the detail information of the image more completely and accurately. And the fusion method based on CCA can give full play to the representation ability of each feature. The facial expression recognition method proposed in this paper obtains a better recognition effect.

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