Feature Disentangling Machine - A Novel Approach of Feature Selection and Disentangling in Facial Expression Analysis
Feature Disentangling Machine - A Novel Approach of Feature Selection and Disentangling in Facial Expression Analysis
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DOI：
10.1007/978-3-319-10593-2_11
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发表时间：
2014-09
期刊：
影响因子：
--
通讯作者：
Ping Liu;Joey Tianyi Zhou;I. Tsang;Zibo Meng;Shizhong Han;Yan Tong
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文献类型：
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作者：
Ping Liu;Joey Tianyi Zhou;I. Tsang;Zibo Meng;Shizhong Han;Yan Tong
心理学研究表明，并不是所有的面部区域都对表情识别有重要作用，不同的面部区域对不同的面部表情有不同的贡献。在此基础上，提出了一种新的人脸表情特征提取框架--特征解缠机。更重要的是，FDM的目标是将这些选定的特征分成不重叠的组，特别是在不同表情之间共享的共同特征和仅对目标表情具有区别性的特定表情特征。该算法将稀疏支持向量机和多任务学习结合在一个统一的框架中，通过构造一种新的损失函数和一组约束条件来精确地控制稀疏性，自然地分离活动特征。在两个著名的面部表情数据库上的广泛实验表明，该方法的性能优于最先进的面部表情分析方法。更重要的是，FDM在跨数据库验证中取得了令人印象深刻的性能，这表明了所选特征的泛化能力。
Studies in psychology show that not all facial regions are of importance in recognizing facial expressions and different facial regions make different contributions in various facial expressions. Motivated by this, a novel framework, named Feature Disentangling Machine (FDM), is proposed to effectively select active features characterizing facial expressions. More importantly, the FDM aims to disentangle these selected features into non-overlapped groups, in particular,common featuresthat are shared across different expressions andexpression-specific featuresthat are discriminative only for a target expression. Specifically, the FDM integrates sparse support vector machine and multi-task learning in a unified framework, where a novel loss function and a set of constraints are formulated to precisely control the sparsity and naturally disentangle active features. Extensive experiments on two well-known facial expression databases have demonstrated that the FDM outperforms the state-of-the-art methods for facial expression analysis. More importantly, the FDM achieves an impressive performance in a cross-database validation, which demonstrates the generalization capability of the selected features.