基于多图融合和改进Xception网络的跨设备手背静脉识别研究

Cross-Device Recognition Research of Dorsal Hand Vein Images Based on Channel Merging and Improved Xception Network

手背静脉是一种新兴的生物特征识别技术,相比其他生物特征具有唯一性、防伪造性、稳定性和非接触性等明显优势;由于采集设备和采集环境的不同,手背静脉灰度图像存在亮度、角度旋转、尺度缩放等差异,识别率较低;由此提出一种基于多图融合和Xception网络的手背静脉识别算法;首先在图像预处理后分割得到二值纹理图,然后将二值图转换为距离图,再由二值图细化得到骨架图;最后融合二值图、距离图和骨架图,得到包含纹理特征和形状特征的三通道合并图;采用Xception结构作为分类网络,并将其激活函数ReLU改为非线性更强的h-swish激活函数;相关实验在由实验室自建的1库和2库两个数据库上进行,其中1库作为训练集,2库作为测试集,最高识别率达到93.54%.

Recognition of dorsal hand vein is an emerging biometric identification technology,which has obvious advantages compared with other biometrics,such as uniqueness,anti-counterfeiting,stability,and non-contact.Due to the difference of the acquisition equipment and acquisition environment,the gray-scale images of the dorsal hand vein have differences in brightness,angle rotation,scale scaling,etc.,so recognition rate is low.Therefore,a dorsal hand vein recognition algorithm based on multi-image fusion and Xception network is proposed.Firstly,a binary texture map is obtained by segmentation after image preprocessing,and then the binary image is transformed into a distance map,and then the skeleton image is achieved through thinning of the binary image.Finally,the binary image,distance image,and skeleton image are combined to obtain a three-channel merged image containing texture features and shape features.The Xception architecture is used as the classification network,and its activation function ReLU is changed to the more nonlinear activation function h-swish.Relevant experiments are carried out on two databases,library 1 and library 2,built by our laboratory.Library 1 is used as training set and library 2 is used as test set.The recognition rate reaches 93.54%.