Facial Expression Recognition via a Boosted Deep Belief Network

Ping Liu, Shizhong Han, Zibo Meng, Yan Tong
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引用次数: 557

Abstract

A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. Through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis.
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基于增强深度信念网络的面部表情识别
面部表情识别的训练过程通常分三个阶段依次进行:特征学习、特征选择和分类器构建。为了获得良好的识别性能,需要大量的实证研究来寻找特征表示、特征集和分类器的最佳组合。本文提出了一种新的增强深度信念网络(boosting Deep Belief Network, BDBN),用于在统一的循环框架中迭代执行三个训练阶段。通过提出的BDBN框架,可以学习和选择一组有效表征与表情相关的面部外观/形状变化的特征,以统计的方式形成增强的强分类器。随着学习的继续,强分类器被迭代改进,更重要的是,根据所选特征对强分类器的相对重要性,通过BDBN框架中的联合微调过程,识别能力也得到加强。在两个公共数据库上进行的大量实验表明,BDBN框架在面部表情分析方面取得了显著的进步。
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