Expression intensity measurement from facial images by self organizing maps
Expression intensity measurement from facial images by self organizing maps
复制标题
DOI：
10.1109/icmlc.2008.4621008
复制
发表时间：
2008-07
期刊：
影响因子：
--
通讯作者：
Md. Ashraful Amin;Hong Yan
中科院分区：
文献类型：
--
作者：
Md. Ashraful Amin;Hong Yan
面部表情识别和从表情中推断情感是一项具有挑战性的任务。已经提出了许多方法来识别面部表情，但更具挑战性的任务ldquofacial expression intensity classificationrdquo仍然不太受关注。在这里，我们提出了一个系统，能够提供一个估计的面部表情强度从面部图像。首先，基于固定模板对这些序列的每个图像进行归一化和裁剪。然后，从这些图像的Gabor小波变换，然后通过主成分分析（PCA）的特征被捕获。最后，自组织映射（SOM）的应用，以确定从这些主成分的情绪强度。在这项工作中，我们提出了一个启发式的MDC（最小距离标准），能够提供一个定量的测量的PC的组合的善良从强度测量的角度来看。此外，我们提出了一种方法来表示的隶属函数的形式SOM的定性性能可视化的结果。
Facial expression recognition and inferring emotion from an expression is a challenging task. Many methods have been proposed to recognize facial expressions, but the more challenging task ldquofacial expression intensity classificationrdquo remains less focused. Here we propose a system that is able to provide an estimation of facial expression intensity from facial images. At first each image of these sequences are normalized and cropped based on a fixed template. Then, features are captured from Gabor wavelet transformation of these images followed by principle component analysis (PCA). Finally, self organizing maps (SOM) are applied to determine the intensity of emotion from these principle components. In this work we propose a heuristic; MDC (minimum distance criterion) that is able to provide a quantitative measurement about the goodness of a combination of PCs from the intensity measurement point of view. Moreover, we propose a method to represent the results of SOM in the form of membership functions to visualize the qualitative performance.