当前位置: X-MOL 学术IEEE Trans. Affect. Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Facial Expression Recognition with Neighborhood-aware Edge Directional Pattern (NEDP)
IEEE Transactions on Affective Computing ( IF 11.2 ) Pub Date : 2020-01-01 , DOI: 10.1109/taffc.2018.2829707
Md Tauhid Bin Iqbal , M. Abdullah-Al-Wadud , Byungyong Ryu , Farkhod Makhmudkhujaev , Oksam Chae

Currently available local feature descriptors used in facial expression recognition at times suffer from unstable feature descriptions, especially in the presence of weak and distorted edges due to noise, limiting their performances. We propose a novel local descriptor named Neighborhood-aware Edge Directional Pattern (NEDP) to overcome such limitations. Instead of relying solely on the local neighborhood to describe the feature around a pixel, as done by the existing local descriptors, NEDP examines the gradients at the target (center) pixel as well as its neighboring pixels to explore a wider neighborhood for the consistency of the feature in spite of the presence of subtle distortion and noise in local region. We introduce template-orientations for the neighboring pixels, which give importance to the gradients in consistent edge directions, prioritizing the specific neighbors falling in the direction of the local edge to represent the shape of the local textures, unambiguously. Moreover, due to the effective management of the featureless regions, no such region is erroneously encoded as a feature by NEDP. Experiments of the performances for person-independent recognition on benchmark expression datasets also show that NEDP performs better than other existing descriptors, and thereby, improves the overall performance of facial expression recognition.

中文翻译:

具有邻域感知边缘方向模式 (NEDP) 的面部表情识别

目前用于面部表情识别的局部特征描述符有时会受到不稳定的特征描述的影响,特别是在由于噪声而存在弱和扭曲边缘的情况下,限制了它们的性能。我们提出了一种名为邻域感知边缘方向模式(NEDP)的新型局部描述符来克服这些限制。NEDP 不是像现有的局部描述符那样仅仅依靠局部邻域来描述像素周围的特征,而是检查目标(中心)像素及其相邻像素的梯度,以探索更广泛的邻域以获得一致性尽管局部区域存在细微的失真和噪声,但该特征仍然存在。我们为相邻像素引入了模板方向,它重视一致边缘方向的梯度,优先考虑落在局部边缘方向的特定邻居,以明确表示局部纹理的形状。此外,由于对无特征区域的有效管理,NEDP 不会将此类区域错误地编码为特征。在基准表情数据集上进行的与人无关的识别性能的实验也表明,NEDP 的性能优于其他现有描述符,从而提高了面部表情识别的整体性能。
更新日期:2020-01-01
down
wechat
bug