基于融合特征提取与 LLE 方法的表情识别

为保证所提取特征表征作用的全面性, 提出一种基于几何特征和局部纹理特征相结合的特征提取方法。 将基于主动表观模型(AAM: Active Appearance Model)特征点标记提取的几何特征和基于局部二值模式(LBP: Local Binary Pattern)提取的眼部和嘴部纹理特征进行融合, 融合后的特征经局部线性嵌入(LLE: Locally Linear Embedding)方法进行特征降维, 并使用多分类的支持向量机(SVM: Support Vector Machine)进行分类识别。 该方法分别选取 JAFFE 数据集 7 类表情和小样本数据集 Yale 的 4 类表情进行实验, 识别准确率分别达到了 98. 57%和 91. 67%, 从而证明了该方法的有效性。

Expression Recognition Based on Fusion Features Extraction and LLE Method

Feature extraction is a basis, a vital step and a major issue in facial expression recognition. To ensure that the extracted features can be more comprehensive characterization of a certain kind of expression, we present a feature extraction method based on fused geometry and local texture features. Geometric features are obtained from the feature points marked by AAM ( Active Appearance Model) algorithm, texture feature extraction is based on LBP ( Local Binary Pattern) algorithm, the dimension of fusion expression features is reduced by LLE ( Locally Linear Embedding ) algorithm. Finally, a multi-class SVM ( Support Vector Machine) is used for facial expression classification. Our method is deployed on the JAFFE and Yale data sets, the results show a recognition accuracy of 98. 57% and 91. 67% respectively, which prove the effectiveness of our proposed method.