论文标题
单眼3D指纹重建和不折扣
Monocular 3D Fingerprint Reconstruction and Unwarping
论文作者
论文摘要
与基于接触的指纹采集技术相比，非接触式采集具有较小的皮肤变形，较大的指纹区域和卫生采集的优势。但是，透视扭曲是非接触式指纹识别的挑战，它会改变脊方向，频率和细节位置，从而导致识别精度降低。我们提出了一种基于学习的形状，从纹理算法中，从单个图像重建了3D手指的形状，并张开原始图像以抑制透视扭曲。无接触式指纹数据库的实验结果表明，所提出的方法具有较高的3D重建精度。对非接触式接触式和非接触式无接触式匹配的匹配实验证明了所提出的方法提高了匹配精度。
Compared with contact-based fingerprint acquisition techniques, contactless acquisition has the advantages of less skin distortion, larger fingerprint area, and hygienic acquisition. However, perspective distortion is a challenge in contactless fingerprint recognition, which changes ridge orientation, frequency, and minutiae location, and thus causes degraded recognition accuracy. We propose a learning based shape from texture algorithm to reconstruct a 3D finger shape from a single image and unwarp the raw image to suppress perspective distortion. Experimental results on contactless fingerprint databases show that the proposed method has high 3D reconstruction accuracy. Matching experiments on contactless-contact and contactless-contactless matching prove that the proposed method improves matching accuracy.
