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首页> 外文会议>International Conference on Computational Science pt.3; 20040606-20040609; Krakow; PL >Image-Based Stress Recognition Using a Model-Based Dynamic Face Tracking System
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Image-Based Stress Recognition Using a Model-Based Dynamic Face Tracking System

机译：基于模型的动态人脸跟踪系统基于图像的压力识别

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Stress recognition from facial image sequences is a subject that has not received much attention although it is an important problem for a host of applications such as security and human-computer interaction. This class of problems and the related software are instances of Dynamic Data Driven Application Systems (DDDAS). This paper presents a method to detect stress from dynamic facial image sequences. The image sequences consist of people subjected to various psychological tests that induce high and low stress situations. We use a model-based tracking system to obtain the deformations of different parts of the face (eyebrows, lips, mouth) in a parameterized form. We train a Hidden Markov Model system using these parameters for stressed and unstressed situations and use this trained system to do recognition of high and low stress situations for an unlabelled video sequence. Hidden Markov Models (HMMs) are an effective tool to model the temporal dependence of the facial movements. The main contribution of this paper is a novel method of stress detection from image sequences of a person's face.
机译：尽管对于许多应用程序（例如安全性和人机交互）来说，这是一个重要的问题，但从面部图像序列进行压力识别仍然是一个备受关注的主题。此类问题和相关软件是动态数据驱动应用程序系统（DDDAS）的实例。本文提出了一种从动态面部图像序列中检测压力的方法。图像序列由经过各种心理测试的人组成，这些心理测试导致高压力和低压力。我们使用基于模型的跟踪系统以参数化形式获取脸部不同部位（眉毛，嘴唇，嘴巴）的变形。我们使用这些参数针对压力和非压力情况训练了隐马尔可夫模型系统，并使用该训练的系统对未标记的视频序列进行了高压力和低压力情况的识别。隐马尔可夫模型（HMM）是对面部运动的时间依赖性进行建模的有效工具。本文的主要贡献是一种从人脸图像序列进行压力检测的新颖方法。

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