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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension
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Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension

机译：通过在线多实例学习与非线性扩展的早期表达式检测

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摘要

Video-based facial expression recognition has received substantial attention over the past decade, while early expression detection (EED) is still a relatively new and challenging problem. The goal of EED is to identify an expression as quickly as possible after the expression starts and before it ends. This timely ability has many potential applications, ranging from human-computer interaction to security. The max-margin early event detector (MMED) is a well-known ranking model for early event detection. It can achieve competitive EED performance but suffers from several critical limitations: 1) MMED lacks flexibility in extracting useful information for segment comparison, which leads to poor performance in exploring the ranking relation between segment pairs; 2) the training process is slow due to the large number of constraints, and the memory requirement is also usually hard to satisfy; and 3) MMED is linear in nature, and hence may not be appropriate for data in a nonlinear feature space. To overcome these limitations, we propose an online multi-instance learning (MIL) framework for EED. In particular, the MIL technique is first introduced to generalize MMED, resulting in the proposed MIL-based EED (MIED), which is more general and flexible than MMED, since various instance construction and combination strategies can be adopted. To accelerate the training process, we reformulate MIED in the online setting and develop online multi-instance learning framework for EED (OMIED). To further exploit the nonlinear structure of the data distribution, we incorporate the kernel methods in OMIED, which results in the proposed online kernel multi-instance learning for early expression detection. Experiments on two popular and one challenging video-based expression data sets demonstrate both the efficiency and effectiveness of the proposed methods.
机译：基于视频的面部表情识别在过去十年中受到了大量的关注，而早期表达检测（EED）仍然是一个相对较新和具有挑战性的问题。 EED的目标是在表达式开始后且在其结束之前尽快识别表达式。这种及时的能力具有许多潜在的应用，从人机与安全性相互作用。 MAX-RAMIN早期事件检测器（MMED）是早期事件检测的公知等级模型。它可以实现有竞争力的EED性能，但遭受了几个关键限制：1）MMED在提取分部比较的有用信息方面缺乏灵活性，这导致探索分段对之间的排名关系的性能不佳; 2）由于大量约束，训练过程缓慢，内存要求通常很难满足; 3）MMED本质上是线性的，因此可能不适合非线性特征空间中的数据。为了克服这些限制，我们提出了一个用于EED的在线多实例学习（MIL）框架。特别地，首先引入MIL技术以概括MMED，导致所提出的基于MIL的EED（MIED），其比MMED更一般，并且灵活，因为可以采用各种实例结构和组合策略。为了加快培训过程，我们在线设置进行了重新做，并在线设置并开发EED（OMIED）的在线多实例学习框架。为了进一步利用数据分布的非线性结构，我们将内核方法纳入遗漏，这导致提出的在线内核多实例学习进行早期表达检测。两个流行的实验和一个具有挑战性的基于视频的表达数据集展示了所提出的方法的效率和有效性。

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