Journal ArticleDOI
Face recognition approach by subspace extended sparse representation and discriminative feature learning
Mengmeng Liao,Xiaodong Gu +1 more
TLDR
Empirical results show that SESRC & LDF achieves the highest recognition rates, outperforming many algorithms including some state-of-the-art ones, such as PLR, MDFR and OPR.About:
This article is published in Neurocomputing.The article was published on 2020-01-15. It has received 27 citations till now. The article focuses on the topics: Feature (machine learning) & Facial recognition system.read more
Citations
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Journal ArticleDOI
A novel facial image recognition method based on perceptual hash using quintet triple binary pattern
TL;DR: A novel face recognition method based on perceptual hash is presented that has a very good classification capability with short execution time and is tested on well-known face datasets.
Journal ArticleDOI
Joint latent low-rank and non-negative induced sparse representation for face recognition
Mingna Wu,Mingna Wu,Shu Wang,Shu Wang,Zhigang Li,Long Zhang,Ling Wang,Zhenwen Ren,Zhenwen Ren +8 more
TL;DR: A Joint Latent Low-Rank and Non-Negative Induced Sparse Representation (JLSRC) for face recognition that seamlessly and elegantly integrates low-rank learning and sparse representation-based classification.
Journal ArticleDOI
Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments
TL;DR: A new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features using the embedded image kernel technique to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features.
Journal ArticleDOI
FoolChecker: A Platform to Evaluate the Robustness of Images against Adversarial Attacks
TL;DR: The FoolChecker platform, presented, presents a platform to evaluate the image robustness against adversarial attacks from the perspective of image itself rather than DNN models, and defines the minimum perceptual distance between the original examples and the adversarial ones to quantify the robustness of images against adversarian attacks.
Journal ArticleDOI
Multiscale face recognition in cluttered backgrounds based on visual attention
Guo Peng,Xiuhua Wan,Du Guoqing,Du Guoqing,Longsheng Wei,Huaiying Lu,Chen Siwei,Changxin Gao,Chen Ying,Jinsheng Li,Dapeng Luo +10 more
TL;DR: This study proposes the attention developmental network to recognize multiscale faces without using face detectors and can attain at least 13% of accuracy improvement over bionic neural networks and ResNet-based recognition networks on the same model scale with less training epochs.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Squeeze-and-Excitation Networks
TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
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Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Proceedings ArticleDOI
FaceNet: A unified embedding for face recognition and clustering
TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
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.