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Titlebook: Biometric Recognition; 10th Chinese Confere Jinfeng Yang,Jucheng Yang,Jianjiang Feng Conference proceedings 2015 Springer International Pub

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樓主: Thoracic
21#
發(fā)表于 2025-3-25 03:18:57 | 只看該作者
Riitta Kosunen,Maria Frick,Jaana Koluotient Image method with 3D Generic Elastic Models (AQI-GEM). Frontal, neutral light face is re-rendered virtually under varying illumination conditions by AQI. Nearly accurate 3D models are constructed from each re-rendered image by GEM so as to virtually synthesize images under varying poses and i
22#
發(fā)表于 2025-3-25 09:20:42 | 只看該作者
Jukka Mettovaara,Jussi Ylikoskiudies on face liveness detection have been performed. In this paper, we cast the face liveness detection problem as a classification problem to distinguish the images of true faces and photo samples based on the rank analysis of sample matrices. We assume that the rank of the true face sample matrix
23#
發(fā)表于 2025-3-25 14:11:18 | 只看該作者
24#
發(fā)表于 2025-3-25 17:01:14 | 只看該作者
25#
發(fā)表于 2025-3-25 22:59:18 | 只看該作者
https://doi.org/10.1007/978-981-10-7239-0ction methods with the help of standalone filter learning and multiscale local feature combination. Such structure cascaded by both linear layers with convolution filters and non-linear layers in binarization process shows better adaptability in different databases. With the help of parallel computi
26#
發(fā)表于 2025-3-26 03:16:24 | 只看該作者
27#
發(fā)表于 2025-3-26 06:52:28 | 只看該作者
28#
發(fā)表于 2025-3-26 10:10:57 | 只看該作者
29#
發(fā)表于 2025-3-26 13:52:06 | 只看該作者
Theoretical Framework of the Study,ave shown satisfying performance on most benchmark datasets. However, its representation is huge. In this paper, we present a novel approach to make Fisher vector compact and improves its performance. We utilize handcrafted low-level descriptors as FV do. However, we retain only 1st order statistics
30#
發(fā)表于 2025-3-26 20:03:24 | 只看該作者
https://doi.org/10.1007/978-981-10-7239-0s, one is face representation and the other is the similarity computation of face vectors. Addressing the two problem, this paper proposes a method for simultaneously learning features and a corresponding similarity metric for a real world face verification, which apply novel regularization to learn
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