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: This work proposes a novel network that extracts multi-scale patch-level deep features, and introduces the well-established normalization techniques in face recognition algorithms into person re-ID, and proposes an improved softmax loss function for learning more compact and more discriminative feature vectors.
19 citations
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TL;DR: This paper proposes a novel loss function named Li-ArcFace based on ArcFace that takes the value of the angle through a linear function as the target logit rather than through cosine function, which has better convergence and performance on low dimensional embedding feature learning for face recognition.
Abstract: With the development of convolutional neural network, significant progress has been made in computer vision tasks. However, the commonly used loss function softmax loss and highly efficient network architecture for common visual tasks are not as effective for face recognition. In this paper, we propose a novel loss function named Li-ArcFace based on ArcFace. Li-ArcFace takes the value of the angle through linear function as the target logit rather than through cosine function, which has better convergence and performance on low dimensional embedding feature learning for face recognition. In terms of network architecture, we improved the the perfomance of MobileFaceNet by increasing the network depth, width and adding attention module. Besides, we found some useful training tricks for face recognition. With all the above results, we won the second place in the deepglint-light challenge of LFR2019.
19 citations
Cites methods from "ArcFace: Additive Angular Margin Lo..."
...In order to obtain highly discriminative embedding features(See Figure 1), a series of novel loss functions have been proposed in recent years, such as A-Softmax [8], CosFace/AM-Softmax [17, 15], ArcFace [4]....
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...Our network architecture is based on a deeper MobileFaceNet(y2) [4], so the residual bottlenecks proposed in MobileNetV2 are used as our main building blocks....
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01 Dec 2020
TL;DR: Zhang et al. as discussed by the authors proposed a single camera masked face detection and identification via the following two approaches: (i) single-step pre-trained YOLO-face/trained yOLOv3 model on the set of known individuals; and (ii) two-step process having pretrained one stage feature pyramid detector network RetinaFace for localizing masked faces and VGGFace2 that generates facial feature vectors for efficient mask face verification.
Abstract: In light of the novel Covid-19 pandemic, wearing masks has been declared mandatory in several institutions and public places for its widespread prevention and public health safety. Under given circumstances, person identification for security purposes including smart-phones face unlock has been a challenging task since the previous practices including both the human authentication by a person as well as by face recognition systems have heavily relied on complete facial features. However, the emergence of large datasets of masked images led to the rapid development of occluded face detection techniques. This paper focuses on single camera masked face detection and identification via the following two approaches: (i) single-step pre-trained YOLO-face/trained YOLOv3 model on the set of known individuals; and (ii) two-step process having pre-trained one stage feature pyramid detector network RetinaFace for localizing masked faces and VGGFace2 that generates facial feature vectors for efficient mask face verification. The dataset employed consists of real-world video examples comprising of 7 individuals with various orientations, illuminations, and occlusions. Experimental results show that RetinaFace and VGGFace2 achieve state-of-the-art results of 92.7% on overall performance, 98.1% face detection, and 94.5% face verification accuracy respectively in 1:1 face mask verification on our custom dataset.
19 citations
17 Jan 2022
TL;DR: This work proposes a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators and outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets.
Abstract: Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreFormer has several benefits compared to prior arts. First, unlike the conventional multi-head self-attention in previous Vision Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention layer to learn fully-spatial interactions between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in ResotreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction, leading to superior restoration results. Third, RestoreFormer outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets, as well as produces images with better visual quality. Code is available at https://github.com/wzhouxiff/RestoreFormer.git.
18 citations
07 Apr 2022
TL;DR: A new open-source benchmarking framework for Visual Geo-localization that allows to build, train, and test a wide range of commonly used ar-chitectures, with the flexibility to change individual components of a geo- localization pipeline is proposed.
Abstract: In this paper, we propose a new open-source benchmarkingframeworkfor Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used ar-chitectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is twofold: i) gaining insights into how differ-ent components and design choices in a VG pipeline im-pact the final results, both in terms of performance (re-call@N metric) and system requirements (such as execution time and memory consumption); ii) establish a system-atic evaluation protocol for comparing different methods. Using the proposed framework, we perform a large suite of experiments which provide criteria for choosing back-bone, aggregation and negative mining depending on the use-case and requirements. We also assess the impact of engineering techniques like pre/post-processing, data aug-mentation and image resizing, showing that better performance can be obtained through somewhat simple procedures: for example, downscaling the images' resolution to 80% can lead to similar results with a 36% savings in ex-traction time and dataset storage requirement. Code and trained models are available at dataset storage requirement. https://deep-vg-bench.herokuapp.com/.
18 citations
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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