人脸性别约束下的深度随机森林表情识别
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湖北科技学院

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TP391

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Facial Expression Recognition using Deep Random Forest under Gender Constraints
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Hubei University of Science and Technology

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    摘要:

    由于人脸表情类内变化和类间干扰因素的存在,导致人脸表情识别仍面临着巨大挑战。本文提出一种基于性别条件约束随机森林的深度人脸表情识别方法,解决人脸表情识别中噪声、性别等变化和干扰问题。首先,采用深度多示例学习方法提取鲁棒性人脸特征,解决人脸光照、遮挡和低分辨率等图像变化问题。其次,采用性别条件随机森林分类方法进行人脸表情分类器设计,解决人脸性别因素干扰问题。在公开的CK+,BU-3DEF,LFW人脸表情数据库上进行广泛实验结果表明:本文方法在三大人脸数据库上分别达到了98.83%、90%、 60.58%的识别率,与先进方法相比具有更好的性能和鲁棒性。另外,与其它先进的深度学习方法(需要大量训练数据库)相比,本文方法只需要小量训练样本就能达到较好效果。

    Abstract:

    Facial expression recognition is still faced with great challenges due to the intra class variation and interclass interference. In this paper, a deep facial expression recognition based on random forest with gender constraints is proposed to solve the problems of noise, gender and other variation and interference. Firstly, robust facial features are extracted by deep multi-instance learning to solve the problems of illumination, occlusion and low resolution. Secondly, the face expression classifier is designed by using gender conditional random forest classification to solve the problem of gender interference. Extensive experiments on the public CK+, BU-3DEF and LFW databases show that the recognition rate of our method is 98.83%, 90% and 60.58% respectively, which has better performance and robustness compared with the state-of-the-art methods. In addition, compared with other advanced deep learning methods (requiring a large number of training databases), our method only needs a small number of training samples to achieve better results.

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历史
  • 收稿日期:2019-12-05
  • 最后修改日期:2021-01-21
  • 录用日期:2020-03-06
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