IEEE Trans Image Process. 2017 Dec;26(12):6006-6018. doi: 10.1109/TIP.2017.2726010. Epub 2017 Jul 11.
This paper presents a new face descriptor, local directional ternary pattern (LDTP), for facial expression recognition. LDTP efficiently encodes information of emotion-related features (ı.e., eyes, eyebrows, upper nose, and mouth) by using the directional information and ternary pattern in order to take advantage of the robustness of edge patterns in the edge region while overcoming weaknesses of edge-based methods in smooth regions. Our proposal, unlike existing histogram-based face description methods that divide the face into several regions and sample the codes uniformly, uses a two-level grid to construct the face descriptor while sampling expression-related information at different scales. We use a coarse grid for stable codes (highly related to non-expression), and a finer one for active codes (highly related to expression). This multi-level approach enables us to do a finer grain description of facial motions while still characterizing the coarse features of the expression. Moreover, we learn the active LDTP codes from the emotion-related facial regions. We tested our method by using person-dependent and independent cross-validation schemes to evaluate the performance. We show that our approaches improve the overall accuracy of facial expression recognition on six data sets.
本文提出了一种新的人脸描述符，局部方向三元模式（LDTP），用于面部表情识别。LDTP 通过使用方向信息和三元模式，有效地编码与情绪相关的特征（即眼睛、眉毛、上鼻和嘴）的信息，从而利用边缘区域中边缘模式的稳健性，同时克服了基于边缘的方法在平滑区域中的弱点。与现有的基于直方图的人脸描述方法不同，我们的方法不是将人脸分成几个区域并均匀地采样代码，而是使用两级网格来构建人脸描述符，同时在不同的尺度上采样与表情相关的信息。我们使用粗网格来表示稳定的代码（与非表情高度相关），使用更细的网格来表示活跃的代码（与表情高度相关）。这种多层次的方法使我们能够更精细地描述面部运动，同时仍然描述表情的粗略特征。此外，我们从与情绪相关的面部区域中学习活跃的 LDTP 代码。我们通过使用依赖于人和独立的交叉验证方案来测试我们的方法，以评估性能。我们表明，我们的方法在六个数据集上提高了面部表情识别的整体准确性。
