光学学报, 2014, 34 (5): 0515001, 网络出版: 2014-04-22   

融合全局与局部多样性特征的人脸表情识别

Fusion of Global and Local Various Feature for Facial Expression Recognition
作者单位
1 燕山大学工业计算机控制工程河北省重点实验室, 河北 秦皇岛 066004
2 国家冷轧板带装备及工艺工程技术研究中心, 河北 秦皇岛 066004
摘要
通常主成分分析(PCA)只能保持数据的全局结构,邻域保持嵌入(NPE)算法只能保持邻域样本间的相似性,忽略了其差异性。针对上述问题,提出了一种融合全局与局部多样性的特征提取算法,并将其应用于人脸表情识别中。该算法利用PCA算法保持全局结构,并通过流形学习思想定义局部差异离散度和局部相似离散度,结合最大局部散度差准则,有效刻画出局部流形结构的多样性;将全局特征和局部多样性特征相结合,提取出低维流形特征用于表情分类。在JAFFE和Cohn-Kanade人脸表情数据库上的实验表明,该算法与PCA、局部保持投影(LPP)、NPE等算法相比,不仅有效地提高了识别率,而且在取得最高识别率时所需维数最低,证明了此算法在识别效果方面的优越性。
Abstract
Principal component analysis (PCA) can only keep the global structure, while neighborhood preserving embedding (NPE) preserves the similarity between neighbor data, but ignores the difference between them. Focusing on the problems mentioned above, a feature extraction method is proposed by fusing global and local various feature, and is applied to facial expression recognition. PCA is used to preserve global structure and a local diversity scatter and a local similarity scatter is defined by manifold learning algorithms, combining with local maximum scatter difference criterion, the proposed method can efficiently preserve the variety of local manifold. The low dimensional feature is extracted by combining the global feature with local various feature for expression classification. The experiments on JAFFE and Cohn-Kanade facial expression databases indicate that compared with PCA, locality preserving progection (LPP), NPE and other methods, this method not only improves the recognition rate efficiently, but also needs the least dimensions when achieves the highest recognition rate, which demonstrates that this method is superior to others in recognition rate.

李雅倩, 李颖杰, 李海滨, 张强, 张文明. 融合全局与局部多样性特征的人脸表情识别[J]. 光学学报, 2014, 34(5): 0515001. Li Yaqian, Li Yingjie, Li Haibin, Zhang Qiang, Zhang Wenming. Fusion of Global and Local Various Feature for Facial Expression Recognition[J]. Acta Optica Sinica, 2014, 34(5): 0515001.

本文已被 8 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!