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Ensemble Classifier Design applied to face expression classification

集成分类器设计应用于人脸表情分类

基本信息

批准号：
EP/E061664/1
负责人：
Terry Windeatt
金额：
$ 35.84万
依托单位：
University of Surrey
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2008
资助国家：
英国
起止时间：
2008 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FE061664%2F1
关键词：
Ensemble Classifier Design applied face

项目摘要

Pattern classification involves assignment of an object to one of several pre-specified categories or classes, and is a key component in many data interpretation activities. The proposed approach focuses on classifiers that learn from examples, and it is assumed that each example pattern is represented by a set of numbers, which are known as the pattern features. In the case of face expression classification (for example distinguish between a smiling and frowning face), these features could consist of numbers representing different aspects of facial features. In order to design a system it is customary to divide the example patterns into two sets, a training set to design the classifier and a test set which is subsequently used to predict the performance when previously unseen examples are applied. A problem arises when there are many features and relatively few examples, and the classifier can learn the training set too well, known as over-fitting so that performance on the test set decreases. The field of ensemble classifiers has been developed to address the problem of achieving the best pattern classification performance using a combination of relatively simple classifiers. It has been found that the combination has the advantage that it is less likely to over-fit. However, there is still the difficulty of tuning the individual classifiers, a process that is normally performed using classifier parameters (for example complexity of a neural network classifier). The common approach is to further divide the training set to produce a validation set that can be used to adjust appropriate parameters. However, when the number of examples is in short supply theses techniques are either inappropriate or very time-consuming.In recent work, the Principal Investigator has developed an ensemble class separability measure that is computed on the training set and that can detect over-fitting. Therefore there is no need for a validation set, thereby making more data available for training. The project proposal is to test the method on real data and confirm the results that have been obtained previously on benchmark data.The technique was proposed for two-class problems, and the proposal is to develop the method for multi-class problems using Error-Correcting-Output-Coding (ECOC). ECOC is an ensemble technique that works by decomposing a multiclass problem into two-class sub-problems. In this proposal the aim is to understand why the technique works well, and to propose a design methodology with the aim of applying it to problems in face expression classification. A further objective is to apply the method to predicting the optimal number of features in feature selection using only the training set. There has been for many years a great deal of effort in discovering the most relevant features, since the result has been shown to be more accurate and efficient classifers. Some early research indicates that using class separability measure, this is a feasible approach. The problem is particulary challenging when there are hudreds or thousands of features,as there are in certain biometric, bio-informatics and data mining applications.It is known that even a small improvement in performance of a pattern classification system can affect commercial viability, and the successful outcome of the project should impact other biometric, bio-informatics and data mining applications. The proposed research is relevant to the EPSRC mission since it is aimed at advancing knowledge and technology with practical application relevance. It is anticipated that the likely result of this research will be the enhancement of UK competitiveness through exploitation of the technology.With the help of project partner Mitsubishi Electric, the developed techniques will be applied to stress analysis for physical security systems and driver fatigue for automotive applications.

模式分类涉及将对象分配到几个预先指定的类别或类中的一个，并且是许多数据解释活动中的关键组成部分。所提出的方法侧重于从示例中学习的分类器，并且假设每个示例模式由一组数字表示，这些数字被称为模式特征。在面部表情分类的情况下（例如区分微笑和皱眉的面部），这些特征可以由表示面部特征的不同方面的数字组成。为了设计一个系统，通常将示例模式分为两组，一组是用于设计分类器的训练集，另一组是随后用于预测应用先前未见过的示例时的性能的测试集。当有许多特征和相对较少的示例时，就会出现问题，并且分类器可以很好地学习训练集，称为过度拟合，从而降低测试集的性能。集成分类器的领域已经被开发来解决使用相对简单的分类器的组合来实现最佳模式分类性能的问题。已经发现，该组合具有不太可能过拟合的优点。然而，仍然存在调整各个分类器的困难，这是一个通常使用分类器参数（例如神经网络分类器的复杂性）执行的过程。常见的方法是进一步划分训练集以产生可用于调整适当参数的验证集。然而，当样本数量不足时，这些技术要么不合适，要么非常耗时。在最近的工作中，首席研究员开发了一种在训练集上计算的集成类可分性度量，可以检测过拟合。因此，不需要验证集，从而使更多的数据可用于训练。该项目的建议是测试的方法对真实的数据和确认的结果，已经获得了基准data.The技术提出了两类问题，并建议是开发的方法，多类问题使用纠错输出编码（ECOC）。ECOC是一种集成技术，它通过将多类问题分解为两类子问题来工作。在这个建议的目的是了解为什么这项技术工作良好，并提出了一个设计方法，其目的是将其应用到人脸表情分类的问题。另一个目的是应用该方法来预测的最佳数量的特征选择，仅使用训练集的功能。多年来，人们一直在努力发现最相关的特征，因为结果已被证明是更准确和有效的分类器。一些早期的研究表明，使用类可分性度量，这是一种可行的方法。这个问题是特别具有挑战性的，当有人类或数以千计的功能，因为有在某些生物识别，生物信息学和数据挖掘应用程序。众所周知，即使是一个小的改进，在性能的模式分类系统可以影响商业可行性，该项目的成功结果应该影响其他生物识别，生物信息学和数据挖掘应用。拟议的研究是相关的EPSRC的使命，因为它的目的是推进知识和技术与实际应用的相关性。预计这项研究的可能结果将是通过利用该技术提高英国的竞争力。在项目合作伙伴三菱电机的帮助下，开发的技术将应用于物理安全系统的应力分析和汽车应用的驾驶员疲劳。