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

只看該作者 · 發(fā)表于 2025-3-21 17:49:50

書(shū)目名稱Biometric Recognition影響因子(影響力)

書(shū)目名稱Biometric Recognition影響因子(影響力)學(xué)科排名

書(shū)目名稱Biometric Recognition網(wǎng)絡(luò)公開(kāi)度

書(shū)目名稱Biometric Recognition網(wǎng)絡(luò)公開(kāi)度學(xué)科排名

書(shū)目名稱Biometric Recognition被引頻次

書(shū)目名稱Biometric Recognition被引頻次學(xué)科排名

書(shū)目名稱Biometric Recognition年度引用

書(shū)目名稱Biometric Recognition年度引用學(xué)科排名

書(shū)目名稱Biometric Recognition讀者反饋

書(shū)目名稱Biometric Recognition讀者反饋學(xué)科排名

只看該作者 · 發(fā)表于 2025-3-21 22:24:33

只看該作者 · 發(fā)表于 2025-3-22 01:28:16

Non-negative Compatible Kernel Construction for Face Recognitionalled the nonnegative in-compatible problem of KNMF. To tackle this problem, this paper presents a new methodology to construct Nonnegative Compatible Kernel (NC-Kernel) for face recognition. We obtain a Nonnegative Nonlinear Mapping (NN-Mapping) by using the techniques of symmetric NMF and nonnegat

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只看該作者 · 發(fā)表于 2025-3-22 14:01:42

Block Statistical Features-based Face Verification on Second Generation Identity Cardttention. This paper proposes a block statistical features(BSF) learning method combining with local Gabor binary pattern(LGBP) for face verification in both second generation identity card(2nd ID card) and video set, which show many differences in biometric caused by age gap and image acquisition c

只看該作者 · 發(fā)表于 2025-3-22 20:21:32

Towards Practical Face Recognition: A Local Binary Pattern Non Frontal Faces Filtering Approachecognition accuracy, in order to solve these problems, we propose non frontal faces filter’s method via support vector machine(SVM) and local binary patterns(LBP). By this method the images with large pose deflection will be filtered. Firstly, we apply the AdaBoost algorithm into real-time face dete

只看該作者 · 發(fā)表于 2025-3-22 23:17:44

只看該作者 · 發(fā)表于 2025-3-23 05:16:40

只看該作者 · 發(fā)表于 2025-3-23 08:59:35

Nonlinear Metric Learning with Deep Convolutional Neural Network for Face Verifications, 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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