LBP+Joint Bayes: Bayesian Face Revisited: A Joint Formulation [paper] [code1] [code2] [code3] LBPFace: Face recognition with local binary patterns [paper] [code] FisherFace(LDA): Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection [paper] [code] EigenFace(PCA): F...
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition 2018 25 MN-vc 92.70% Multicolumn Networks for Face Recognition 2018 26 FaceNet 66.5% FaceNet: A Unified Embedding for Face Recognition and Clustering 2015...
SphereFace+(MHE): Learning towards Minimum Hyperspherical Energy [paper] [code] MobileFace: A face recognition solution on mobile device [code] MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices [paper] [code1] [code2][code3] [code4] FaceID: An impleme...
FaceNet: A Unified Embedding for Face Recognition and Clustering davidsandberg/facenet • • CVPR 2015 On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%. 179 Paper Code DisguiseNet : A Contrastive Approach for Disguised Face...
machine-learning deep-learning artificial-intelligence face face-recognition face-detection face-landmark face-expression face-clustering face-manipulation face-anti-spoofing face-3d face-benchmark face-action face-gan face-deblurring face-super-resolution face-paper face-code awesome-face Updated Jun 3...
FaceNet is a deep neural network used for extracting features from an image of a person’s face. It was proposed in 2015 by three Google Researchers, Florian Schroff, Dmitry Kalenichenko, and James Philbin, in the paper titledFaceNet: A Unified Embedding for Face Recognition and Clustering.It...
From this result, we hypothesized that objects with a simple configuration, such as faces, can induce stronger clustering in the latent space representation than those of other object classes and therefore may have a greater likelihood of generating units selective to them. To validate our ...
On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a measure that achieves better effectiveness of clustering. In this paper, we ...
machine-learningdeep-learningartificial-intelligencefaceface-recognitionface-detectionface-landmarkface-expressionface-clusteringface-manipulationface-anti-spoofingface-3dface-benchmarkface-actionface-ganface-deblurringface-super-resolutionface-paperface-codeawesome-face ...
一旦产生了欧几里德空间嵌入,则上述任务变得直截了当:人脸验证仅涉及一个距离的阀值,即两个嵌入的欧几里德空间距离阀值;人脸识别变成了一个k-NN分类问题;聚类可以通过诸如k-means 或者agglomerative clustering(层次聚类凝聚)的先进技术去实现。 Previous face recognition approaches based on deep networks use a classi...