Face Identification through Learned Image High Feature Video Frame Works
Boya Akhila,Burgubai Jyothi +1 more
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This article is published in International journal of scientific research.The article was published on 2018-08-31 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Feature (computer vision) & Frame (networking).read more
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Proceedings ArticleDOI
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
TL;DR: This work revisits both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network.
Proceedings ArticleDOI
Face recognition in unconstrained videos with matched background similarity
Lior Wolf,Tal Hassner,Itay Maoz +2 more
TL;DR: A comprehensive database of labeled videos of faces in challenging, uncontrolled conditions, the ‘YouTube Faces’ database, along with benchmark, pair-matching tests are presented and a novel set-to-set similarity measure, the Matched Background Similarity (MBGS), is described.
Proceedings ArticleDOI
Discriminative Deep Metric Learning for Face Verification in the Wild
Junlin Hu,Jiwen Lu,Yap-Peng Tan +2 more
TL;DR: The proposed DDML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold.
Proceedings ArticleDOI
Probabilistic Elastic Matching for Pose Variant Face Verification
TL;DR: This work proposes a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy.
Proceedings ArticleDOI
Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild
TL;DR: A new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks from an image and achieves the state-of-the-art performances on two real-world datasets LFW and YouTube Faces according to the restricted protocol.