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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：通过带有非线性扩展的在线多实例学习进行早期表情检测

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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：在过去的十年中，基于视频的面部表情识别受到了广泛的关注，而早期表情检测（EED）仍然是一个相对较新且具有挑战性的问题。 EED的目标是在表达式开始之后和结束之前尽快识别一个表达式。这种及时的功能具有许多潜在的应用程序，从人机交互到安全性。最大余量的早期事件检测器（MMED）是用于早期事件检测的众所周知的排名模型。它可以实现具有竞争力的EED性能，但存在几个关键限制：1）MMED在提取有用的信息以进行段比较时缺乏灵活性，这导致在探索段对之间的排名关系方面表现不佳； 2）由于大量的约束，训练过程缓慢，并且通常也难以满足存储需求； 3）MMED本质上是线性的，因此可能不适用于非线性特征空间中的数据。为了克服这些限制，我们提出了针对EED的在线多实例学习（MIL）框架。特别是，首先引入了MIL技术以对MMED进行一般化，从而导致提出了基于MIL的EED（MIED），它比MMED更通用，更灵活，因为可以采用各种实例构造和组合策略。为了加快培训过程，我们在在线环境中重新制定了MIED，并开发了EED（OMIED）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，我们在OMIED中引入了内核方法​​，从而提出了用于早期表达检测的在线内核多实例学习方法。在两个流行的和一个具有挑战性的基于视频的表达数据集上进行的实验证明了所提出方法的效率和有效性。

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