论文标题
使用基于面部形状的傅立叶描述融合的人类表达识别
Human Expression Recognition using Facial Shape Based Fourier Descriptors Fusion
论文作者
论文摘要
动态面部表达识别在社交网络，多媒体内容分析，安全系统等中具有许多有用的应用。在图像照明和低分辨率的反复出现问题下，必须在部分闭塞下改变。本文旨在根据面部肌肉的变化产生一种新的面部表达识别方法。几何特征用于指定面部区域，即嘴巴，眼睛和鼻子。通用傅里叶形状描述符与椭圆形傅立叶形状描述符结合使用，用作代表频谱特征下不同情绪的属性。之后，将多类支持向量机应用于七个人类表达的分类。统计分析表明，我们使用5倍的交叉验证获得了总体胜任的识别，并在众所周知的面部表达数据集上精确地识别。
Dynamic facial expression recognition has many useful applications in social networks, multimedia content analysis, security systems and others. This challenging process must be done under recurrent problems of image illumination and low resolution which changes at partial occlusions. This paper aims to produce a new facial expression recognition method based on the changes in the facial muscles. The geometric features are used to specify the facial regions i.e., mouth, eyes, and nose. The generic Fourier shape descriptor in conjunction with elliptic Fourier shape descriptor is used as an attribute to represent different emotions under frequency spectrum features. Afterwards a multi-class support vector machine is applied for classification of seven human expression. The statistical analysis showed our approach obtained overall competent recognition using 5-fold cross validation with high accuracy on well-known facial expression dataset.
