卷积神经网络的发展极大促进了面部表情识别技术的进步,为解决实际应用中图像识别的准确率受图像像素限制的问题,从三方面对低像素人脸图像的表情识别进行研究。首先根据研究对象像素低、特征复杂的特点,提出了一种改进的卷积神经网络。其次对图像进行基本的预处理操作后,又增加了图像增强处理,作为改进卷积神经网络模型的输入。最后将模型的输出结果进行决策融合,得到最终的识别结果。实验表明,该方法在CK+数据集上取得了良好的效果,且识别准确率较高、效果稳定、泛化能力强。

The development of convolutional neural networks has greatly promoted the advancement of facial expression recognition technology. In order to solve the problem that the accuracy of image recognition in practical applications is limited by image pixels, the expression recognition of low pixel face image is studied from three aspects. First, according to the characteristics of low pixels and complex features of the research object, an improved convolutional neural network is proposed. Second, after performing basic pre-processing on the image, image enhancement processing is added as an input to improve the convolutional neural network mode. Finally, the output results of the model are subjected to decision fusion to obtain the final recognition result. Experimental results show that this method has achieved good results on the CK+ source dataset, and has high recognition accuracy, stable results, and strong generalization ability.