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.
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TL;DR: This work presents a methodology to identify individual TR ISO-fueled pebbles by exploiting the unique distribution of the TRISO-coated particles, which is imprinted during the manufacturing process, within individual TRISO -fueledpebbles.
3 citations
3 citations
TL;DR: Wang et al. as mentioned in this paper proposed a novel multi-view, multi-spectral 3D finger imaging system, which is capable of capturing almost all finger-based traits, such as fingerprint, finger vein, finger knuckle, finger shape, and so on.
Abstract: Fingers contain various discriminative biometric traits, such as fingerprint, finger vein, finger knuckle, finger shape, and so on, which are complementary in identity information. However, only one or a few traits are used in most current research and practical applications, while others are ignored, resulting in degraded recognition performance and vulnerability to forgery. In this paper, we make the first attempt to collect and study all biometric traits on the finger. Firstly, a novel multi-view, multi-spectral 3D finger imaging system is proposed. To the best of our knowledge, it is the first biometric imaging system capable of capturing almost all finger-based traits. We scanned numerous fingers with this 3D finger imaging system, obtaining external skin images and internal vein images from 6 different views. The proposed 3D finger reconstruction and texture mapping algorithms are then used to generate 3D finger models with skin and vein textures. Second, we establish a benchmark dataset, namely the Large-scale Finger Multi-Biometric database and benchmark for 3D Finger Biometrics (LFMB-3DFB). The LFMB-3DFB contains 695 fingers, and each finger is acquired 10 times, yielding 6 finger skin images and 6 finger vein images for a total of 83,400 images and 6,950 3D finger models. Finally, we design a more scientific and comprehensive evaluation protocol to conduct extensive experimental research and analysis on this database for both subject-independent verification and subject-independent close-set identification tasks. Comprehensive and rigorous experiments for 2D finger traits recognition, multi-view finger traits recognition, 3D finger traits recognition, and score-level fusion on LFMB-3DFB are carried out, and excellent results are achieved. The LFMB-3DFB database will be released at https://github.com/SCUT-BIP-Lab/LFMB-3DFB to promote 3D finger multi-biometric research using cutting-edge imaging techniques.
3 citations
TL;DR: In this paper , the authors exploit Semi-Supervised Learning (SSL) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization (FGVC ).
Abstract: In this article, we exploit Semi-Supervised Learning ( SSL ) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization ( FGVC ). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model. This combination allows one to back-propagate the information of the parts, represented by second-order pooling, onto unlabeled data in an adversarial training setting. We demonstrate the effectiveness of the combined use by conducting experiments on six state-of-the-art fine-grained datasets, which include Aircrafts, Stanford Cars, CUB-200-2011, Oxford Flowers, Stanford Dogs, and the recent Semi-Supervised iNaturalist-Aves. Experimental results clearly show that our proposed method has better performance than the only previous approach that examined this problem; it also obtained higher classification accuracy with respect to the supervised learning methods with which we compared.
3 citations
10 Jan 2021
TL;DR: In this article, the authors perform a thorough study of several recent state-of-the-art losses commonly used in face recognition task for deepfake classification and detection since the current deepfake is highly related to face generation.
Abstract: Due to the recent breakthroughs of deep generative models, the fake faces, also known as deepfake which has been abused to deceive the general public, can be easily produced at scale and in very high fidelity. Many works focus on exploring various network architectures or various artifacts produced by deep generative models. Instead, in this work, we focus on the loss functions which have been shown to play a significant role in the context of face recognition. We perform a thorough study of several recent state-of-the-art losses commonly used in face recognition task for deepfake classification and detection since the current deepfake is highly related to face generation. With extensive experiments on the challenging FaceForensic++ and Celeb-DF datasets, the evaluation results provide a clear overview of the performance comparisons of different loss functions and generalization capability across different deepfake data.
3 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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08 Dec 2014
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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