Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea.
IEEE Trans Image Process. 2013 May;22(5):1740-52. doi: 10.1109/TIP.2012.2235848. Epub 2012 Dec 21.
This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information, and we encode such information using the prominent direction indices (directional numbers) and sign-which allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks.
本文提出了一种新的局部特征描述符，局部方向数模式（LDN），用于面部分析，即面部和表情识别。LDN 以紧凑的方式对人脸纹理的方向信息进行编码（即纹理的结构），生成比现有方法更具判别力的代码。我们借助指南针掩模计算每个微模式的结构，该掩模提取方向信息，并使用突出的方向索引（方向数）和符号对其进行编码，这使我们能够区分具有不同强度过渡的相似结构模式。我们将人脸划分为几个区域，并从中提取 LDN 特征的分布。然后，我们将这些特征连接成一个特征向量，并将其用作人脸描述符。我们进行了多次实验，结果表明我们的描述符在光照、噪声、表情和时间推移变化下表现一致。此外，我们使用不同的掩模来测试我们的描述符，以分析其在不同面部分析任务中的性能。
