Emotion plays an important role in communication. For human–computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications used in real-life conditions, to gain better results without DNNs, in this paper, we propose an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and Local Binary Patterns (LBP) features extracted from facial expression. First of all, every image is passed through face detection algorithm to extract more effective features. Second, in order to increase computational speed, the ORB and LBP features are extracted from the face region; specifically, region division is innovatively employed in the traditional ORB to avoid the concentration of the features. The features are invariant to scale and grayscale as well as rotation changes. Finally, the combined features are classified by Support Vector Machine (SVM). The proposed method is evaluated on several challenging databases such as Cohn-Kanade database (CK+), Japanese Female Facial Expressions database (JAFFE), and MMI database; experimental results of seven emotion state (neutral, joy, sadness, surprise, anger, fear, and disgust) show that the proposed framework is effective and accurate.

中文翻译：

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具有 LBP 和 ORB 特征的面部表情识别


情感在沟通中起着重要作用。对于人机交互来说，面部表情识别已经成为不可或缺的一部分。最近，深度神经网络（DNN）在该领域得到广泛应用，它们克服了传统方法的局限性。然而，由于过高的硬件规格要求，DNN 的应用非常有限。考虑到现实生活中使用的硬件规格较低，为了在没有 DNN 的情况下获得更好的结果，本文提出了一种结合了定向 FAST 和旋转的 Brief (ORB) 特征以及从 DNN 中提取的局部二进制模式 (LBP) 特征的算法。表情。首先，每张图像都经过人脸检测算法，提取更有效的特征。其次，为了提高计算速度，从人脸区域中提取ORB和LBP特征；具体来说，在传统的ORB中创新性地采用了区域划分，避免了特征的集中。这些特征对于尺度和灰度以及旋转变化是不变的。最后，通过支持向量机（SVM）对组合特征进行分类。所提出的方法在几个具有挑战性的数据库上进行了评估，例如 Cohn-Kanade 数据库（CK+）、日本女性面部表情数据库（JAFFE）和 MMI 数据库；七种情绪状态（中性、快乐、悲伤、惊讶、愤怒、恐惧和厌恶）的实验结果表明，所提出的框架是有效和准确的。