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.
Citations
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TL;DR: Li et al. as discussed by the authors proposed a self-supervised Siamese inference network to improve the generalization and robustness of encoders, which can encode contextual semantics from full-resolution images and obtain more discriminative representations.
4 citations
23 May 2022
TL;DR: In this article , the authors provide two datasets and corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs.
Abstract: Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. This paper bridges this gap by providing two datasets and the corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We also conduct extensive experiments to assess the vulnerability of four state-of-the-art face recognition systems, including FaceNet, VGG-Face, ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although visually more appealing, morphs based on StyleGAN 2 do not pose a significant threat to the state to face recognition systems, as these morphs were outmatched by the simple morphs that are based facial landmarks.
4 citations
Posted Content•
TL;DR: The AISpeech-SJTU system for the accent identification track of the Interspeech 2020 Accented English Speech Recognition Challenge as discussed by the authors achieved the best accuracy of 83.63% on the challenge evaluation data.
Abstract: This paper describes the AISpeech-SJTU system for the accent identification track of the Interspeech-2020 Accented English Speech Recognition Challenge. In this challenge track, only 160-hour accented English data collected from 8 countries and the auxiliary Librispeech dataset are provided for training. To build an accurate and robust accent identification system, we explore the whole system pipeline in detail. First, we introduce the ASR based phone posteriorgram (PPG) feature to accent identification and verify its efficacy. Then, a novel TTS based approach is carefully designed to augment the very limited accent training data for the first time. Finally, we propose the test time augmentation and embedding fusion schemes to further improve the system performance. Our final system is ranked first in the challenge and outperforms all the other participants by a large margin. The submitted system achieves 83.63\% average accuracy on the challenge evaluation data, ahead of the others by more than 10\% in absolute terms.
4 citations
21 Aug 2022
TL;DR: Huber et al. as discussed by the authors proposed a specialized, likelihood-based fusion method to enable deep learning-based face recognition on historic portrait paintings and also proposed a method to accurately determine the confidence of the made decision to assist art historians in their research.
Abstract: Verifying the identity of a person (sitter) portrayed in a historical painting is often a challenging but critical task in art historian research. In many cases, this information has been lost due to time or other circumstances and today there are only speculations of art historians about which person it could be. Art historians often use subjective factors for this purpose and then infer from the identity information about the person depicted in terms of his or her life, status, and era. On the other hand, automated face recognition has achieved a high level of accuracy, especially on photographs, and considers objective factors to determine the identity or verify a suspected identity. The limited amount of data, as well as the domain-specific challenges, make the use of automated face recognition methods in the domain of historic paintings difficult. We propose a specialized, likelihood-based fusion method to enable deep learning-based face recognition on historic portrait paintings. We additionally propose a method to accurately determine the confidence of the made decision to assist art historians in their research. For this purpose, we used a model trained on common photographs and adapted it to the domain of historical paintings through transfer learning. By using an underlying challenge dataset, we compute the likelihood for the assumed identity against reference images of the identity and fuse them to utilize as much information as possible. From these results of the likelihoods fusion, we then derive decision confidence to make statements to determine the certainty of the model’s decision. The experiments were carried out in a leave-one-out evaluation scenario on our created database, the largest authentic database of historic portrait paintings to date, consisting of over 760 portrait paintings of 210 different sitters by over 250 different artists. The experiments demonstrated, that a) the proposed approach outperforms pure face recognition solutions, b) the fusion approach effectively combines the sitter information towards a higher verification accuracy, and c) the proposed confidence estimation approach is highly successful in capturing the estimated accuracy of the decision. The meta-information of the used historic face images can be found at https://github.com/marcohuber/HistoricalFaces.
4 citations
TL;DR: Paco as discussed by the authors is a large dataset of part masks and attributes of common objects with 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets.
Abstract: Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets. We provide 641K part masks annotated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/facebookresearch/paco.
4 citations
References
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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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10,447 citations