Titlebook: Biometric Recognition; 12th Chinese Confere Jie Zhou,Yunhong Wang,Shiqi Yu Conference proceedings 2017 Springer International Publishing AG

countenance

dyspareunia

0302-9743 ics; iris; gesture and gait; emerging biometrics;. voice and speech; video surveillance; feature extraction and classification theory; behavioral. biometrics..978-3-319-69922-6978-3-319-69923-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

aspect

Language Socialization and Identity,of feature is optimized by dimensionality reduction and feature selection. Then, we investigate the optimal fusion strategy of multiple types of features. The results show that the fusion of AAM, LBP, and PCANet features obtains the best performance, which can serve as a competitive baseline for further studies.

Femine

https://doi.org/10.1007/978-3-319-63239-1ernatively and both of them are treated as the supervised learning tasks. The AlexNet and ResNet-50 architectures are adopted as the classifiers and the Gaussian form of the label distribution is assumed. Experiments show that the accuracy of age estimation can be improved by refining label distribution.

讨厌

Language Learning in Anglophone Countriest in terms of the consistency of adjacent pixels are effectively exploited. With the DLBP feature, the detection is accomplished by using a Softmax classifier. Experiments are done with four public benchmark databases, and the results indicate its effectiveness both in intra-database and cross-database testing.

民间传说

Language Learning in Anglophone Countries recognition, and compare their performance in terms of face verification accuracy. We further combine the Depth-to-RGB matcher with the RGB-to-RGB matcher via score-level fusion. Evaluation experiments on two databases demonstrate that matching depth to RGB does boost face verification accuracy.

MILL

Rosamond Mitchell,Nicole Tracy-Venturaty Face greatly decreases. 20 pairs of the same or different Internet Celebrity Faces are selected to test the human’s ability to recognize Internet Celebrity Faces by questionnaire. The comparison with the VGG deep network shows that the deep learning algorithm performs much better than human in terms of recognition accuracy.