一种鲁棒的基于对抗结构的生物特征 ROI 提取方法

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.