{"title":"基于局部均值二值模式的面部表情识别","authors":"Mahesh M. Goyani, N. Patel","doi":"10.5565/REV/ELCVIA.1058","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel appearance based local feature extraction technique called Local Mean Binary Pattern (LMBP), which efficiently encodes the local texture and global shape of the face. LMBP code is produced by weighting the thresholded neighbor intensity values with respect to mean of 3 x 3 patch. LMBP produces highly discriminative code compared to other state of the art methods. The micro pattern is derived using the mean of the patch, and hence it is robust against illumination and noise variations. An image is divided into M x N regions and feature descriptor is derived by concatenating LMBP distribution of each region. We also propose a novel template matching strategy called Histogram Normalized Absolute Difference (HNAD) for comparing LMBP histograms. Rigorous experiments prove the effectiveness and robustness of LMBP operator. Experiments also prove the superiority of HNAD measure over well-known template matching methods such as L2 norm and Chi-Square measure. We also investigated LMBP for facial expression recognition low resolution. The performance of the proposed approach is tested on well-known datasets CK, JAFFE, and TFEID.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"10 1","pages":"54-67"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Recognition of Facial Expressions using Local Mean Binary Pattern\",\"authors\":\"Mahesh M. Goyani, N. Patel\",\"doi\":\"10.5565/REV/ELCVIA.1058\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel appearance based local feature extraction technique called Local Mean Binary Pattern (LMBP), which efficiently encodes the local texture and global shape of the face. LMBP code is produced by weighting the thresholded neighbor intensity values with respect to mean of 3 x 3 patch. LMBP produces highly discriminative code compared to other state of the art methods. The micro pattern is derived using the mean of the patch, and hence it is robust against illumination and noise variations. An image is divided into M x N regions and feature descriptor is derived by concatenating LMBP distribution of each region. We also propose a novel template matching strategy called Histogram Normalized Absolute Difference (HNAD) for comparing LMBP histograms. Rigorous experiments prove the effectiveness and robustness of LMBP operator. Experiments also prove the superiority of HNAD measure over well-known template matching methods such as L2 norm and Chi-Square measure. We also investigated LMBP for facial expression recognition low resolution. The performance of the proposed approach is tested on well-known datasets CK, JAFFE, and TFEID.\",\"PeriodicalId\":38711,\"journal\":{\"name\":\"Electronic Letters on Computer Vision and Image Analysis\",\"volume\":\"10 1\",\"pages\":\"54-67\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronic Letters on Computer Vision and Image Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5565/REV/ELCVIA.1058\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Letters on Computer Vision and Image Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5565/REV/ELCVIA.1058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 11
摘要
本文提出了一种新的基于外观的局部特征提取技术——局部平均二值模式(LMBP),该技术可以有效地编码人脸的局部纹理和全局形状。LMBP代码是通过对阈值邻居强度值相对于3 x 3 patch的平均值进行加权而产生的。与其他最先进的方法相比,LMBP产生高度判别的代码。微图案是使用补丁的平均值导出的,因此它对光照和噪声变化具有鲁棒性。将图像划分为M × N个区域,并通过连接每个区域的LMBP分布得到特征描述符。我们还提出了一种新的模板匹配策略,称为直方图归一化绝对差(HNAD),用于比较LMBP直方图。实验证明了LMBP算子的有效性和鲁棒性。实验还证明了HNAD测度相对于L2范数、卡方测度等模板匹配方法的优越性。我们还研究了LMBP在低分辨率面部表情识别中的应用。在知名数据集CK、JAFFE和TFEID上测试了该方法的性能。
Recognition of Facial Expressions using Local Mean Binary Pattern
In this paper, we propose a novel appearance based local feature extraction technique called Local Mean Binary Pattern (LMBP), which efficiently encodes the local texture and global shape of the face. LMBP code is produced by weighting the thresholded neighbor intensity values with respect to mean of 3 x 3 patch. LMBP produces highly discriminative code compared to other state of the art methods. The micro pattern is derived using the mean of the patch, and hence it is robust against illumination and noise variations. An image is divided into M x N regions and feature descriptor is derived by concatenating LMBP distribution of each region. We also propose a novel template matching strategy called Histogram Normalized Absolute Difference (HNAD) for comparing LMBP histograms. Rigorous experiments prove the effectiveness and robustness of LMBP operator. Experiments also prove the superiority of HNAD measure over well-known template matching methods such as L2 norm and Chi-Square measure. We also investigated LMBP for facial expression recognition low resolution. The performance of the proposed approach is tested on well-known datasets CK, JAFFE, and TFEID.