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

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31#
發(fā)表于 2025-3-27 01:02:46 | 只看該作者
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發(fā)表于 2025-3-27 05:46:33 | 只看該作者
Language Learning with Technology as their combinations are evaluated by experiments, and the underlying principle of the experimental results is investigated. According to our investigation, it is almost impossible to attain a satisfied face recognition result by using only one facial descriptor/representation especially under dra
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發(fā)表于 2025-3-27 21:35:37 | 只看該作者
Piotr Stalmaszczyk,Wies?aw Oleksy wide application potential in real condition. In this paper, we present a novel 3D aided face recognition method that can deal with the probe images in different viewpoints. It first estimates the face pose based on the Random Regression Forest, and then rotates the 3D face models in the gallery se
37#
發(fā)表于 2025-3-28 01:23:32 | 只看該作者
Using Model Essays to Create Good Writersd and proven to be useful for human face gender recognition. However, they have lots of shortcomings, such as, requiring setting a large number of training parameters, difficultly choosing the appropriate parameters, and much time consuming for training. In this paper, we proposes a new learning met
38#
發(fā)表于 2025-3-28 02:19:09 | 只看該作者
Agnieszka Skrzypek,David Singleton. In this paper, we propose a simple but efficient facial IQA algorithm based on Bayesian fusion of modified Structural Similarity (mSSIM) index and Support Vector Machine (SVM) as a reduced-reference method for facial IQA. The fusion scheme largely improves the facial IQA and consequently promotes
39#
發(fā)表于 2025-3-28 08:20:53 | 只看該作者
40#
發(fā)表于 2025-3-28 13:47:53 | 只看該作者
https://doi.org/10.1007/978-981-16-4001-8ape is represented statistically by a set of well-defined landmark points and its variations are modeled by the principal component analysis (PCA). However, we find that both PCA and Procrustes analysis are sensitive to noise, and there is a linear relationship between alignment error and magnitude
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