Titlebook: Biometric Recognition; 7th Chinese Conferen Wei-Shi Zheng,Zhenan Sun,Jianhuang Lai Conference proceedings 2012 Springer-Verlag Berlin Heide

搭话

书目名称Biometric Recognition影响因子(影响力)




书目名称Biometric Recognition影响因子(影响力)学科排名




书目名称Biometric Recognition网络公开度




书目名称Biometric Recognition网络公开度学科排名




书目名称Biometric Recognition被引频次




书目名称Biometric Recognition被引频次学科排名




书目名称Biometric Recognition年度引用




书目名称Biometric Recognition年度引用学科排名




书目名称Biometric Recognition读者反馈




书目名称Biometric Recognition读者反馈学科排名

terazosin

https://doi.org/10.1007/978-981-99-9643-8n; (2) When the fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase; (3) When the area of partial fingerprints is smaller than 20,000 pixels (about fifth of the normal full fingerprints), the performance of partial fingerprint rec

CURT

https://doi.org/10.1057/9781137404862can not only achieve a lower EER (1.71%) than LBP (2.67%), but also can overcome the difficulties of rotation in rotated databases. The EERs of proposed method in three different rotated databases are 2.15%, 2.98% and 2.75% respectively, which shows that our method has better robustness in rotation

先兆

assail

On the Influence of Fingerprint Area in Partial Fingerprint Recognitionn; (2) When the fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase; (3) When the area of partial fingerprints is smaller than 20,000 pixels (about fifth of the normal full fingerprints), the performance of partial fingerprint rec

covert

SENT

NICHE

仲裁者

Complete Gradient Face: A Novel Illumination Invariant Descriptorures, especially the gradient based descriptor [13], is an effective tool to solve this issue. In this paper, we propose a novel gradient based descriptor, namely Complete Gradient Face (CGF), to compensate the limitations in [13] and contribute in three folds: (1) we incorporate homogeneous filteri

针叶

A Face Authentication Scheme Based on Affine-SIFT (ASIFT) and Structural Similarity (SSIM)IM). The ASIFT descriptor defines key points which are used to match the gallery and probe face images. The matched pairs of key points are filtered based on the location of points in the gallery face image. Then the similarity between sub-images at a preserved pair of matched points is measured by