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: This paper proposes a novel adaptive aggregation scheme based on ordered weighted average (OWA) operators in contrast with the mainly used weighted mean scheme, and develops two different concrete implementations to validate its suitability for video face recognition.
12 citations
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TL;DR: Zhang et al. as mentioned in this paper proposed a novel Domain Balancing (DB) mechanism to handle the long-tailed domain distribution problem, which refers to the fact that a small number of domains frequently appear while other domains far less existing.
Abstract: Long-tailed problem has been an important topic in face recognition task. However, existing methods only concentrate on the long-tailed distribution of classes. Differently, we devote to the long-tailed domain distribution problem, which refers to the fact that a small number of domains frequently appear while other domains far less existing. The key challenge of the problem is that domain labels are too complicated (related to race, age, pose, illumination, etc.) and inaccessible in real applications. In this paper, we propose a novel Domain Balancing (DB) mechanism to handle this problem. Specifically, we first propose a Domain Frequency Indicator (DFI) to judge whether a sample is from head domains or tail domains. Secondly, we formulate a light-weighted Residual Balancing Mapping (RBM) block to balance the domain distribution by adjusting the network according to DFI. Finally, we propose a Domain Balancing Margin (DBM) in the loss function to further optimize the feature space of the tail domains to improve generalization. Extensive analysis and experiments on several face recognition benchmarks demonstrate that the proposed method effectively enhances the generalization capacities and achieves superior performance.
12 citations
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TL;DR: In this paper, the face image is represented in the embedding domain using two disentangled but complementary representations, and the network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image.
Abstract: In this paper, we present a novel differential morph detection framework, utilizing landmark and appearance disentanglement. In our framework, the face image is represented in the embedding domain using two disentangled but complementary representations. The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image. This initially trained network is further trained for each dataset using contrastive representations. We demonstrate that, by employing appearance and landmark disentanglement, the proposed framework can provide state-of-the-art differential morph detection performance. This functionality is achieved by the using distances in landmark, appearance, and ID domains. The performance of the proposed framework is evaluated using three morph datasets generated with different methodologies.
12 citations
TL;DR: Zhang et al. as mentioned in this paper proposed a DSA-Face model to learn diverse and sparse attentions, which can extract diverse discriminative local representations and suppress the focus on noisy regions.
Abstract: Learning local representations is important for face recognition (FR). Recent attention-based networks emphasize few facial parts, while ignoring other potentially discriminative ones. This is more serious when there are large pose variations, occlusions (e.g. face masks), or other image quality changes. To address this, we propose Diverse and Sparse Attentions, called DSA-Face. First, a divergence loss is designed to explicitly encourage the diversity among multiple attention maps by maximizing the Euclidean distance between every pair attention maps. As a result, a Pairwise Self-Contrastive Attention (PSCA) is developed to locate diverse facial parts which provide comprehensive descriptions. Second, an Attention Sparsity Loss (ASL) is proposed to encourage sparse responses in attention maps where only discriminative parts are emphasized while distracted regions (e.g. background or face masks) are discouraged. Built upon the PSCA and ASL, the DSA-Face model is developed to learn diverse and sparse attentions, which can extract diverse discriminative local representations and suppress the focus on noisy regions. Due to the pandemic of the COVID-19, the task of masked face matching is now very important, and our model can handle this much better than previous methods, demonstrating its effectiveness and usefulness. Moreover, our model outperforms the state-of-the-art methods on several other FR benchmarks, showing that it is also general to address various challenges in FR.
12 citations
TL;DR: A new loss function called Dyn-arcFace(Dynamic Additive Angular Margin Loss for Deep Face Recognition) is proposed, which can reduce the degree of overfitting caused by the fixed additive angular margin.
Abstract: Deep convolutional neural networks (CNNs) are widely used in face recognition, because they can extract features with higher discrimination, which is the basis for correctly identifying the identity of a face image. In order to improve the face recognition performance, in addition to improving the structures of convolutional neural networks, many new loss functions have been proposed to enhance the distinguishing ability of extract features. However, according to our research, it is found that when using the present loss functions in CNNs, there is overfitting of the training dataset and redeuces the effect of face recognition. Therefore, a new loss function called Dyn-arcFace(Dynamic Additive Angular Margin Loss for Deep Face Recognition) is proposed in this paper. In Dyn-arcFace, the traditional fixed additive angular margin is developed into a dynamic one, which can reduce the degree of overfitting caused by the fixed additive angular margin. To verify the effect of Dyn-arcFace, we tested on different layers of neural networks. The proposed algorithm achieved state-of-the-art performance on the most popular public-domain face recognition datasets.
12 citations
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