Robust Facial Expression Recognition Based on Local Directional Pattern
Robust Facial Expression Recognition Based on Local Directional Pattern
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DOI：
10.4218/etrij.10.1510.0132
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发表时间：
2010-10-01
期刊：
影响因子：
1.4
通讯作者：
Chae, Oksam
中科院分区：
文献类型：
--
作者：
Jabid, Taskeed;Kabir, Md. Hasanul;Chae, Oksam
人脸表情识别在人机交互的各个领域都有着广泛的应用前景。然而，由于缺乏有效的面部特征描述符，它们尚未完全实现。在本文中，我们提出了一种新的基于外观的特征描述符，局部方向模式（LDP），来表示面部几何形状，并分析其在表情识别中的性能。通过计算每个像素处8个方向上的边缘响应值并使用这些边缘响应的相对强度将它们编码成8位二进制数来获得LDP特征。LDP描述符是图像或图像块内LDP代码的分布，用于描述每个表情图像。降维技术，如主成分分析和AdaBoost的有效性，也分析了计算成本节省和分类精度。两个著名的机器学习方法，模板匹配和支持向量机，用于分类使用科恩-Kanade和日本女性面部表情数据库。更好的分类精度显示了LDP描述符相对于其他基于外观的特征描述符的优越性。
Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.