ArcFace: Additive Angular Margin Loss for Deep Face Recognition
15 Jun 2019-pp 4690-4699
TL;DR: This paper presents arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks, and shows that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead.
Abstract: One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that can enhance the discriminative power. Centre loss penalises the distance between deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in the angular space and therefore penalises the angles between deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximise face class separability. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to its exact correspondence to geodesic distance on a hypersphere. We present arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks which includes a new large-scale image database with trillions of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead. To facilitate future research, the code has been made available.
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TL;DR: In this article, a pedestrian detection system based on convolutional neural networks is proposed to be implemented in an advanced driver assistance system (ADAS) with a variety of applications.
Abstract: The growth in the number of vehicles in the world makes it hard to safely share the environment with pedestrians. Pedestrian’s safety is an important task that needs to be granted in the traffic environment. New cars are equipped with advanced driver assistance systems (ADAS) with a variety of applications. Pedestrian detection application is one of the most important applications for an ADAS that needs to be enhanced. In this paper, we propose a pedestrian detection system to be implemented in an ADAS. The proposed system is based on convolutional neural networks thanks to its performance when solving computer vision applications. On the other side, the proposed system ensures real-time processing and high detection performance. The proposed system will be designed by tacking the advantage of building lightweight convolution blocks and model compression techniques to ensure an embedded implementation. Those blocks will guarantee high precision and fast processing speed. To train and evaluate the proposed system, we used the Caltech dataset. The evaluation of the proposed system resulted in 87% of mean average precision and an inference speed of 35 frames per second.
22 citations
07 Mar 2022
TL;DR: A novel face protection method aiming at constructing adversarial face images that preserve stronger black-box transferability and better visual quality simultaneously, and introduces a new regularization module along with a joint training strategy to reconcile the conflicts between the adversarial noises and the cycle consistence loss in makeup transfer.
Abstract: While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks. Recently, some studies adopt adversarial examples to protect photos from being identified by unauthorized face recognition systems. However, existing methods of generating adversarial face images suffer from many limitations, such as awkward visual, white-box setting, weak transferability, making them difficult to be applied to protect face privacy in reality. In this paper, we propose adversarial makeup transfer GAN (AMT-GAN) 11https://github.com/CGCL-codes/AMT-GAN, a novel face protection method aiming at constructing adversarial face images that preserve stronger black-box transferability and better visual quality simultaneously. AMT-GAN leverages generative adversarial networks (GAN) to synthesize adversarial face images with makeup transferred from reference images. In particular, we introduce a new regularization module along with a joint training strategy to reconcile the conflicts between the adversarial noises and the cycle consistence loss in makeup transfer, achieving a desirable balance between the attack strength and visual changes. Extensive experiments verify that compared with state of the arts, AMT-GAN can not only preserve a comfortable visual quality, but also achieve a higher attack success rate over commercial FR APIs, including Face++, Aliyun, and Microsoft.
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TL;DR: A cancelable multi-biometric face recognition method that uses multiple convolutional neural networks (CNNs) to extract deep features from different facial regions and a new CNN architecture that exploits batch normalization, depth concatenation and a residual learning framework is proposed.
Abstract: In this paper, we propose a cancelable multi-biometric face recognition method that uses multiple convolutional neural networks (CNNs) to extract deep features from different facial regions. We also propose a new CNN architecture that exploits batch normalization, depth concatenation and a residual learning framework. The proposed method adopts a region-based technique in which face, eyes, nose and mouth regions are detected from the original face images. Multiple CNNs are used to extract deep features from each region, and then, a fusion network combines these features. Moreover, to provide user’s privacy and increase the system resistance against spoof attacks, a cancelable biometric technique using bio-convolving encryption is performed on the final facial descriptor. Our experiments on the FERET, LFW and PaSC datasets show excellent and competitive results compared to state-of-the-art methods in terms of recognition accuracy, specificity, precision, recall and fscore.
22 citations
01 Oct 2019
TL;DR: A novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users’ invariant features is proposed.
Abstract: The evolution of social media users’ behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, naive approaches may fail to generalize to new users or even to future observations of previously known users. In this paper, we propose a novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users’ invariant features. We fit the model by optimizing a surrogate metric learning objective over a large corpus of unlabeled social media content. Once learned, the mapping may be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. We present a comprehensive evaluation to validate the benefits of the proposed approach using data from Reddit, Twitter, and Wikipedia.
22 citations
Cites methods from "ArcFace: Additive Angular Margin Lo..."
...Following Deng et al. (2019) we again introduce a weight matrix W ∈ RY×D whose rows now serve as class centers for the training authors....
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...For the angular margin loss we take m = 0.5 and s = 64 as suggested in Deng et al. (2019)....
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TL;DR: In this paper , the influence of 47 attributes on the verification performance of two popular face recognition models was investigated on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations.
Abstract: Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user’s demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyze FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group-based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many nondemographic attributes strongly affect recognition performance, such as accessories, hairstyles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making the FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, enhance the robustness of these networks, and develop more generalized bias-mitigating FR solutions.
22 citations
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28 Oct 2017
TL;DR: An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead.
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13,268 citations
Posted Content•
TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
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computational devices such as GPU cards. The system is flexible and can be used
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reference implementation were released as an open-source package under the
Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
10,447 citations