基于显著区域的面部表情识别

Anh H. Vo, Bao T. Nguyen
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引用次数: 4

摘要

面部表情识别在人机交互、患者监护、神经病学、社交机器人等领域得到了广泛的应用,虽然人脸表情识别取得了一些令人鼓舞的成果,但仍面临着光照变化、模糊等诸多挑战。特别是,识别悲伤和愤怒的表达会增加更多的障碍。在本文中,我们提出了一个仅使用显著面部区域的框架,但它能够提高面部表情识别的准确性。在该框架中,采用了一种最先进的描述符——局部相位量化金字塔描述符(PLPQ)来对图像模糊进行鲁棒处理。该实验在扩展的Cohn-Canade (CK+)数据库上实现了97.7%的准确率识别率,优于其他先进的面部表情识别方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facial Expression Recognition Based on Salient Regions
Facial expression recognition has been applied in many fields such as human computer interaction, patient monitoring, neurology, social robot… Although facial expression recognition has gained some encourage results, there are still many challenges such as the change of illumination, blur, etc. Especially, recognizing between sadness and anger expression raises more barriers. In this paper, we proposed a framework using only salient facial regions, but it would be able to improve the accuracy of facial expression recognition. In this framework, one of the most state-of-the-art descriptor, called Pyramid of Local Phase Quantization descriptor (PLPQ) was used to robust with respect to image blur. The experiment achieved 97.7% accuracy recognition rate on the extend Cohn-Canade (CK+) database, and outperformed than other state-of-the-art facial expression recognition methods.
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