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
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.