基于VGGNet和多谱带循环网络的高光谱人脸识别系统

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。

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

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.