Deep face recognition: A survey
TL;DR: 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.
Citations
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TL;DR: In this paper, the authors investigated the impact of facial tattoos and paintings on the performance of face recognition systems and showed that facial tattoos affect all the tested modules, especially for images where a large area of the face is covered with tattoos or paintings.
Abstract: In the past years, face recognition technologies have shown impressive recognition performance, mainly due to recent developments in deep convolutional neural networks. Notwithstanding those improvements, several challenges which affect the performance of face recognition systems remain. In this work, we investigate the impact that facial tattoos and paintings have on current face recognition systems. To this end, we first collected an appropriate database containing image-pairs of individuals with and without facial tattoos or paintings. The assembled database was used to evaluate how facial tattoos and paintings affect the detection, quality estimation, as well as the feature extraction and comparison modules of a face recognition system. The impact on these modules was evaluated using state-of-the-art open-source and commercial systems. The obtained results show that facial tattoos and paintings affect all the tested modules, especially for images where a large area of the face is covered with tattoos or paintings. Our work is an initial case-study and indicates a need to design algorithms which are robust to the visual changes caused by facial tattoos and paintings.
4 citations
4 citations
05 Dec 2022
TL;DR: Wang et al. as discussed by the authors proposed an enhanced EEG authentication framework with motor imagery, which integrates signal preprocess, channel selection and deep learning classification to provide an end-to-end authentication.
Abstract: Electroencephalography (EEG) is the record of electrogram of the electrical activity on the scalp typically using non-invasive electrodes. In recent years, many studies started using EEG as a human characteristic to construct biometric identification or authentication. Being a kind of behavioral characteristics, EEG has its natural advantages whereas some characteristics have not been fully evaluated. For instance, we find that Motor Imagery (MI) brain-computer interface is mainly used for improving neurological motor function, but has not been widely studied in EEG authentication. Currently, there are many mature methods for understanding such signals. In this paper, we propose an enhanced EEG authentication framework with Motor Imagery, by offering a complete EEG signal processing and identity verification. Our framework integrates signal preprocess, channel selection and deep learning classification to provide an end-to-end authentication. In the evaluation, we explore the requirements of a biometric system such as uniqueness, permanency, collectability, and investigate the framework regarding insider and outsider attack performance, cross-session performance, and influence of channel selection. We also provide a large comparison with state-of-the-art methods, and our experimental results indicate that our framework can provide better performance based on two public datasets.
4 citations
4 citations
TL;DR: A deep learning architecture that implements Deep Residual Equivariant Mapping (DREAM) to improve pose-robustness of a lightweight MobileFaceNets model as a solution to the underlying issue is proposed.
4 citations
References
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TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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