基于VGGNet和多谱带循环网络的高光谱人脸识别系统

Hyperspectral face recognition system based on VGGNet and multi-band recurrent network

为了提高光谱人脸数据表征人脸特征的有效性,提出一种基于VGGNet和多谱带循环训练的高光谱人脸识别方法。首先,在光谱人脸图像的预处理阶段,采用多任务卷积神经网络(MTCNN)进行高光谱人脸图像的精确定位,并利用混合通道的方式对高光谱人脸数据进行增强;然后,基于卷积神经网络(CNN)结构建立一个面向高光谱人脸识别的VGG12深度网络;最后,基于高光谱人脸数据的特点,引入多谱带循环训练方法训练建立的VGG12网络,完成最后的训练和识别。在公开的UWA-HSFD和Poly U-HSFD高光谱人脸数据集的实验结果表明,所提方法取得了比其他深度网络(如Deep ID、Deep Face、VGGNet)更好的识别性能。

To improve the effectiveness of facial feature represented by hyperspectral face data,a VGGNet and multi-band recurrent training based method for hyperspectral face recognition was proposed.Firstly,a Multi-Task Convolutional Neural Network(MTCNN)was used to locate the hyperspectral face image accurately in preprocessing phase,and the hyperspectral face data was enhanced by mixed channel.Then,a Convolutional Neural Network(CNN)structure based VGG12 deep network was built for hyperspectral face recognition.Finally,multi-band recurrent training was introduced to train the VGG12 network and realize the recognition based on the characteristics of hyperspectral face data.The experimental results of UWA-HSFD and PolyU-HSFD databases reveal that the proposed method is superior to other deep networks such as DeepID,DeepFace and VGGNet.