改进深度学习块卷积神经网络的人脸表情识别

设计一种改进的块卷积神经网络架构,并结合主动形状模型和局部二元模式映射实现人脸表情识别。采用主动形状模型定位人脸关键点,实现人脸姿态校正和感兴趣区域抽取;对校正后的图像进行局部二元模式映射,降低光照干扰;设计改进的卷积神经网络架构,对局部二元模式图像和感兴趣区域两个输入项进行学习和训练,建立分类器并实现人脸表情分类。人脸表情识别实验结果表明,该方法识别率高,运算效率较高。

Facial expression recognition with modified region-based convolutional neural networks

A modified frame of region-based convolutional neural networks was designed and used to realize facial expression re- cognition by combining active shape models with local binary pattern mapping. Active shape model was used to locate key points of face image, for correcting face posture and extracting regions of interest. Local binary pattern mapping was executed on corrected image, to reduce illumination interference. A modified frame of region-based convolutional neural networks was designed, to learn and train two input of local binary pattern image and regions of interest, and classifier was constructed and facial expression classification was realized. Experimental results of facial expression recognition show that, the proposed method has not only high recognition rate, and also higher efficiency.