基于曲面类型与深度学习融合的三维掌纹识别技术

传统的2维掌纹识别在图像采集时容易受到干湿度、残影和压力等影响,使得其鲁棒性和准确性降低.为解决这些问题,3维掌纹识别技术应运而生.现有的3维掌纹身份认证技术需要将掌纹的特征提取与匹配识别分开进行,不仅延缓了识别时间,更增加了不同方法优化组合的难度.该文提出一种基于曲面类型(ST)与深度学习融合的3维掌纹识别方法.该方...

Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning

Traditional Two-Dimensional (2D) palmprint recognition is susceptible to the effects of dry humidity, residual image and pressure during image acquisition, which reduces its robustness and accuracy. To solve these problems, Three-Dimensional (3D) palmprint recognition technology is widely studied. The existing 3D palmprint identity authentication technology needs to separate palmprint feature extraction and matching recognition, which not only delays the recognition time, but also increases the difficulty of optimizing the combination of different methods. A 3D palmprint recognition method is proposed based on the fusion of Surface Type (ST) and deep learning. ST images is used to represent 3D palmprint features and to be as input of Convolutional Neural Network (CNN) to realize training. The test image can be automatically extracted the feature information of the palmprint image and complete the identification directly. The experimental results show that the proposed method has an accuracy of 99.43% and a recognition time of 28 ms on the public data set, which has high performance of accuracy and speed compared with the traditional 3D palmprint recognition methods.