Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
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TLDR
A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.About:
This article is published in Neurocomputing.The article was published on 2021-03-14 and is currently open access. It has received 353 citations till now. The article focuses on the topics: Deep learning & Feature extraction.read more
Citations
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MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.
Karl Ricanek,Tamirat Tesafaye +1 more
TL;DR: It is concluded that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work, and the efficacy of this algorithm is evaluated against the variables of gender and racial origin.
References
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Proceedings ArticleDOI
Deep Transfer Network with 3D Morphable Models for Face Recognition
TL;DR: A novel approach that uses a deep transfer network (DTN) with 3D morphable models (3DMMs) for face recognition to overcome the shortage of labeled face images and the dataset bias between synthesized images and corresponding real images and can improve the performance of the convolutional neural network (CNN) model.
Posted Content
On the Capacity of Face Representation.
TL;DR: The proposed model estimates the capacity of a 128-dimensional DNN based face representation, FaceNet, and that of the classical Eigenfaces representation of the same dimensionality, to establish an upper bound on the scalability of an automatic face recognition system.
Proceedings ArticleDOI
Unleash the Black Magic in Age: A Multi-Task Deep Neural Network Approach for Cross-Age Face Verification
TL;DR: This paper presents an end-to-end learning framework for cross-age face verification by designing a multi-task deep neural network architecture that exploits the intrinsic low-dimensional representation shared between the tasks of face verification and age estimation.
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MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
TL;DR: Li et al. as discussed by the authors proposed a lightweight deep neural network named MobiFace, which is able to achieve high performance with 99.73% on LFW database and 91.3% on large-scale challenging Megaface database.
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Towards Transferable Adversarial Attack against Deep Face Recognition
Yaoyao Zhong,Weihong Deng +1 more
TL;DR: DFANet as mentioned in this paper is a dropout-based method used in convolutional layers, which can increase the diversity of surrogate models and obtain ensemble-like effects to improve the transferability of feature-level adversarial examples.