基于深度学习网络的近红外人脸表情识别

Near infrared facial expression recognition based on deep learning networks

近红外人脸表情识别主要依赖图像局部特征,提取特征受到干扰时,人脸表情识别准确率低。因此,设计深度学习网络的新型近红外人脸表情识别方法。依托于图像局部优化保留法重建图像结构信息,得到降维后的近红外人脸图像。应用点分布模型检测出人脸上所有关键点,抽取出人脸表情识别的感兴趣区域,运用深度学习网络架构搭建人脸表情分类识别模型,通过调整识别模型的参数得到人脸表情的识别结果。实验结果表明:所提方法识别结果的Acc平均值达到了0.95,很大程度提升了近红外人脸表情识别准确性。

Near infrared facial expression recognition mainly relies on local features of lazy images.When the extracted features are interfered with,the accuracy of facial expression recognition is low.Therefore,a new near-infrared facial expression recognition method based on deep learning networks is designed.Relying on the local optimization and preservation method of the image to reconstruct the image structure information,the reduced dimensionality near-infrared facial image is obtained.The application point distribution model detects all key points on the face,extracts regions of interest for facial expression recognition,and constructs a facial expression classification and recognition model using a deep learning network architecture.By adjusting the parameters of the recognition model,the recognition results of facial expressions are obtained.The experimental results show that the average Acc value of the proposed method's recognition results reaches 0.95,greatly improving the accuracy of near-infrared facial expression recognition.