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Expression recognition algorithm based on the relative relationship of the facial landmarks

机译:基于人脸标志物相对关系的表情识别算法

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In order to improve the facial expression recognition accuracy in complex contexts and decrease the identification time, this paper presents an efficient expression recognition algorithm based on feature points of the facial landmarks. In practice, facial expressions mainly focus on a few parts of muscle activity, which provides valuable reference to infer human emotions and intentions. Facial expression is a powerful nonverbal way for human to transmit information and reveal emotion. In this paper, we focus on geometric positions of key parts of the face. Firstly, the face area is detected in a photo or video, then the key parts of the face is extracted and the position correction is performed. A set of key points is located using the relative position of the face. This process can not only effectively avoid the impact of the environment and the light on the sample, but also greatly improve the recognition of facial expressions. With the development of human-computer interaction, facial expression recognition has become a hot topic in the field of pattern recognition. After years of development, facial expression recognition has achieved some success such as HOG. In this paper, HOG feature extraction method of facial expression is used as a contrast. Experimental results show that the proposed algorithm can extract key information and achieves higher recognition accuracy.
机译:为了提高复杂场景下人脸表情识别的准确性,减少识别时间,提出了一种基于人脸界标特征点的高效人脸表情识别算法。在实践中,面部表情主要集中在肌肉活动的几个部分,这为推断人类的情绪和意图提供了宝贵的参考。面部表情是人类传递信息和揭示情感的一种强有力的非语言方式。在本文中,我们重点研究面部关键部位的几何位置。首先,在照片或视频中检测脸部区域,然后提取脸部的关键部分并执行位置校正。使用面部的相对位置来定位一组关键点。该过程不仅可以有效避免环境和光线对样品的影响,而且可以大大提高面部表情的识别能力。随着人机交互的发展,面部表情识别已成为模式识别领域的热门话题。经过多年的发展,面部表情识别已经取得了一些成功,例如HOG。本文将人脸表情的HOG特征提取方法作为对比。实验结果表明,该算法能够提取关键信息,并具有较高的识别精度。

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