In this paper, we propose a novel method for finger-vein recognition. We extract the features of the vein patterns for recognition. Then, the minutiae features included bifurcation points and ending points are extract...In this paper, we propose a novel method for finger-vein recognition. We extract the features of the vein patterns for recognition. Then, the minutiae features included bifurcation points and ending points are extracted from these vein patterns. These feature points are used as a geometric representation of the vein patterns shape. Finally, the modified Hausdorff distance algorithm is provided to evaluate the identifica-tion ability among all possible relative positions of the vein patterns shape. This algorithm has been widely used for comparing point sets or edge maps since it does not require point cor-respondence. Experimental results show these minutiae feature points can be used to perform personal verification tasks as a geometric rep-resentation of the vein patterns shape. Fur-thermore, in this developed method. we can achieve robust image matching under different lighting conditions.展开更多
Finger vein biometrics have been extensively studied for the capability to detect aliveness,and the high security as intrinsic traits.However,vein pattern distortion caused by finger rotation degrades the performance ...Finger vein biometrics have been extensively studied for the capability to detect aliveness,and the high security as intrinsic traits.However,vein pattern distortion caused by finger rotation degrades the performance of CNN in 2D finger vein recognition,especially in a contactless mode.To address the finger posture variation problem,we propose a 3D finger vein verification system extracting axial rotation invariant feature.An efficient 3D finger vein reconstruction optimization model is proposed and several accelerating strategies are adopted to achieve real-time 3D reconstruction on an embedded platform.The main contribution in this paper is that we are the first to propose a novel 3D point-cloud-based endto-end neural network to extract deep axial rotation invariant feature,namely 3DFVSNet.In the network,the rotation problem is transformed to a permutation problem with the help of specially designed rotation groups.Finally,to validate the performance of the proposed network more rigorously and enrich the database resources for the finger vein recognition community,we built the largest publicly available 3D finger vein dataset with different degrees of finger rotation,namely the Large-scale Finger Multi-Biometric Database-3D Pose Varied Finger Vein(SCUT LFMB-3DPVFV)Dataset.Experimental results on 3D finger vein datasets show that our 3DFVSNet holds strong robustness against axial rotation compared to other approaches.展开更多
文摘In this paper, we propose a novel method for finger-vein recognition. We extract the features of the vein patterns for recognition. Then, the minutiae features included bifurcation points and ending points are extracted from these vein patterns. These feature points are used as a geometric representation of the vein patterns shape. Finally, the modified Hausdorff distance algorithm is provided to evaluate the identifica-tion ability among all possible relative positions of the vein patterns shape. This algorithm has been widely used for comparing point sets or edge maps since it does not require point cor-respondence. Experimental results show these minutiae feature points can be used to perform personal verification tasks as a geometric rep-resentation of the vein patterns shape. Fur-thermore, in this developed method. we can achieve robust image matching under different lighting conditions.
文摘Finger vein biometrics have been extensively studied for the capability to detect aliveness,and the high security as intrinsic traits.However,vein pattern distortion caused by finger rotation degrades the performance of CNN in 2D finger vein recognition,especially in a contactless mode.To address the finger posture variation problem,we propose a 3D finger vein verification system extracting axial rotation invariant feature.An efficient 3D finger vein reconstruction optimization model is proposed and several accelerating strategies are adopted to achieve real-time 3D reconstruction on an embedded platform.The main contribution in this paper is that we are the first to propose a novel 3D point-cloud-based endto-end neural network to extract deep axial rotation invariant feature,namely 3DFVSNet.In the network,the rotation problem is transformed to a permutation problem with the help of specially designed rotation groups.Finally,to validate the performance of the proposed network more rigorously and enrich the database resources for the finger vein recognition community,we built the largest publicly available 3D finger vein dataset with different degrees of finger rotation,namely the Large-scale Finger Multi-Biometric Database-3D Pose Varied Finger Vein(SCUT LFMB-3DPVFV)Dataset.Experimental results on 3D finger vein datasets show that our 3DFVSNet holds strong robustness against axial rotation compared to other approaches.
