Novel directional patterns and a Generalized Supervised Dimension Reduction System (GSDRS) for facial emotion recognition
Novel directional patterns and a Generalized Supervised Dimension Reduction System (GSDRS) for facial emotion recognition
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DOI：
10.1007/s11042-017-5141-8
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发表时间：
2018-04-01
影响因子：
3.6
通讯作者：
Dharma, Dejey
中科院分区：
文献类型：
--
作者：
Alphonse, A. Sherly;Dharma, Dejey
本文提出了两种新的方向模式，基于最大响应的方向纹理模式（MRDTP）和基于最大响应的方向数模式（MRDNP），用于识别在约束以及无约束的情况下的面部情绪。在应用八个Kirsch掩模之后，从边缘响应的最大值获得的强度信息用于MRDTP中的面部特征的计算。在MRDNP中，代替强度信息，使用最大响应的方向数。在将MRDNP和MRDTP代码图像划分成网格之后，从从网格获得的级联直方图创建特征向量。本文还提出了一个有效的广义监督降维系统（GSDRS），并使用极端学习机与径向基函数（ELM-RBF）分类器的快速和有效的情感分类。这两种模式在去除随机噪声和使用突出边缘提供良好的结构信息方面比现有模式更有效，这有助于在七个数据集上实现高分类精度。
This paper presents two novel directional patterns, a Maximum Response-based Directional Texture Pattern (MRDTP) and a Maximum Response-based Directional Number Pattern (MRDNP), for recognizing the facial emotions in constrained as well as unconstrained situations. The intensity information obtained from the maximum of the edge responses, after applying eight Kirsch masks, is used for the calculation of facial features in MRDTP. In MRDNP, instead of intensity information, the direction number of the maximum response is used. After dividing MRDNP and MRDTP code images into grids, feature vectors are created from the concatenated histograms obtained from the grids. This paper also proposes an effective Generalized Supervised Dimension Reduction System (GSDRS) and uses Extreme Learning Machine with Radial Basis Function (ELM-RBF) classifier for rapid and efficient classification of emotions. Both the proposed patterns are more effective than the existing ones in removing random noise and providing good structural information using prominent edges which help to achieve high classification accuracy when tested with seven datasets.