一种鲁棒的基于对抗结构的生物特征ROI提取方法

A Robust ROI Extraction Method for Biometrics Using Adversarial Structure

感兴趣区域(Region of interest,ROI)提取在生物特征识别中,常用于减少后续处理的计算消耗,提高识别模型的准确性,是生物识别系统中预处理的关键步骤.针对生物识别数据,提出了一种鲁棒的ROI提取方法.方法使用语义分割模型作为基础,通过增加全局感知模块,与分割模型形成对抗结构,为模型提供先验知识,补充全局视觉模式信息,解决了语义分割模型的末端收敛困难问题,提高了模型的鲁棒性和泛化能力.在传统二维(2D)指纹、人脸、三维(3D)指纹和指纹汗孔数据集中验证了方法的有效性.实验结果表明,相比于现有方法,所提出的ROI提取方法更具鲁棒性和泛化能力,精度最高.

Region of interest(ROI)extraction is an initial and key step in biometrics since it can not only facilitate more accurate feature extraction but also can reduce the computational cost.This paper proposes a more robust ROI extraction method for biometric image.The method uses semantic segmentation network as the basis.By adding the global perceptual loss module(i.e.,adversarial structure)into the loss function of the learning model,prior knowledge is provided to try to make the model know the global pattern information.Furthermore,global perceptual loss module solves the problem of terminal convergence and improve the robustness of the ROI extraction.The effectiveness of the proposed method is validated on the 2D fingerprint,face,3D fingerprint and sweat pore datasets,respectively.Comparisons with other ROI extraction methods also shows the outstanding performance of the proposed method.