Titlebook: Deep Learning for Biometrics; Bir Bhanu,Ajay Kumar Book 2017 Springer International Publishing AG, part of Springer Nature 2017 Deep Learn

離開浮于空中

書目名稱Deep Learning for Biometrics影響因子(影響力)




書目名稱Deep Learning for Biometrics影響因子(影響力)學(xué)科排名




書目名稱Deep Learning for Biometrics網(wǎng)絡(luò)公開度




書目名稱Deep Learning for Biometrics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning for Biometrics被引頻次




書目名稱Deep Learning for Biometrics被引頻次學(xué)科排名




書目名稱Deep Learning for Biometrics年度引用




書目名稱Deep Learning for Biometrics年度引用學(xué)科排名




書目名稱Deep Learning for Biometrics讀者反饋




書目名稱Deep Learning for Biometrics讀者反饋學(xué)科排名

AMPLE

辭職

CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detectionacial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenario

道學(xué)氣

Latent Fingerprint Image Segmentation Using Deep Neural NetworkRBMs), and uses it to perform segmentation of latent fingerprint images. Artificial neural networks (ANN) are biologically inspired architectures that produce hierarchies of maps through learned weights or filters. Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a

TEN

Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashingnal privacy and anonymity in during the identification process. The Convolutional Neural Network (CNN) has shown remarkable capability for learning biometric features that can offer robust and accurate matching. We introduce a new approach for the finger vein authentication using the CNN and supervi

IRATE

IRATE

纖細(xì)

genuine

幾何學(xué)家