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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Proceedings Article•
08 Feb 2022
TL;DR: Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization.
Abstract: Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to kNN relations in the feature space, which may lead to a lot of noise edges connecting two faces of different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the performance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. It significantly reduces the noise edges while maintaining the good ones to build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization. Code is available at https://github.com/damo-cv/Ada-NETS.
12 citations
27 Sep 2022
TL;DR: The core idea is to leverage a style-based generator to empower high-fidelity and robust face swapping, thus the generator’s advantage can be adopted for optimizing identity similarity.
Abstract: Numerous attempts have been made to the task of person-agnostic face swapping given its wide applications. While existing methods mostly rely on tedious network and loss designs, they still struggle in the information balancing between the source and target faces, and tend to produce visible artifacts. In this work, we introduce a concise and effective framework named StyleSwap. Our core idea is to leverage a style-based generator to empower high-fidelity and robust face swapping, thus the generator's advantage can be adopted for optimizing identity similarity. We identify that with only minimal modifications, a StyleGAN2 architecture can successfully handle the desired information from both source and target. Additionally, inspired by the ToRGB layers, a Swapping-Driven Mask Branch is further devised to improve information blending. Furthermore, the advantage of StyleGAN inversion can be adopted. Particularly, a Swapping-Guided ID Inversion strategy is proposed to optimize identity similarity. Extensive experiments validate that our framework generates high-quality face swapping results that outperform state-of-the-art methods both qualitatively and quantitatively.
11 citations
01 Nov 2020
TL;DR: In this article, the authors investigated the expressivity of facial attributes for two different deep convolutional neural network (DCNN) architectures: a Resnet-101 and an Inception Resnet v2.
Abstract: As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces While these networks are solely trained to recognize identities, they also contain face related information such as sex, age, and pose of the face even when the networks are not trained to learn these attributes We introduce expressivity as a measure of how much a feature vector informs us about an attribute, where a feature vector can be from internal or final layers of a network Expressivity is computed by a second neural network whose inputs are features and attributes The output of the second neural network approximates the mutual information between feature vectors and an attribute We investigate the expressivity for two different deep convolutional neural network (DCNN) architectures: a Resnet-101 and an Inception Resnet v2 In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw Additionally, we studied the changes in the encoding of facial attributes over training iterations We found that as training progresses, expressivities of yaw, sex, and age decrease Our technique can be a tool for investigating the sources of bias in a network and a step towards explaining the network’s identity decisions
11 citations
TL;DR: The hardware-friendly pruning quantization (HFPQ) method proposed in this paper trains the network after pruning and then quantizes the weights, which effectively combines layered channel pruning with quantization by a power exponential of 2.
Abstract: This paper presents a hardware-friendly compression method for deep neural networks. This method effectively combines layered channel pruning with quantization by a power exponential of 2. While keeping a small decrease in the accuracy of the network model, the computational resources for neural networks to be deployed on the hardware are greatly reduced. These computing resources for hardware resolution include memory, multiple accumulation cells (MACs), and many logic gates for neural networks. Layered channel pruning groups the different layers by decreasing the model accuracy of the pruned network. After pruning each layer in a specific order, the network is retrained. The pruning method in this paper sets a parameter, that can be adjusted to meet different pruning rates in practical applications. The quantization method converts high-precision weights to low-precision weights. The latter are all composed of 0 and powers of 2. In the same way, another parameter is set to control the quantized bit width, which can also be adjusted to meet different quantization precisions. The hardware-friendly pruning quantization (HFPQ) method proposed in this paper trains the network after pruning and then quantizes the weights. The experimental results show that the HFPQ method compresses VGGNet, ResNet and GoogLeNet by 30+ times while reducing the number of FLOPs by more than 85%.
11 citations
25 Oct 2020
TL;DR: The authors proposed domain-balanced hard prototype mining to finetune the state-of-the-art ECAPA-TDNN x-vector based speaker embedding extractor.
Abstract: In this paper we describe the top-scoring IDLab submission for the text-independent task of the Short-duration Speaker Verification (SdSV) Challenge 2020. The main difficulty of the challenge exists in the large degree of varying phonetic overlap between the potentially cross-lingual trials, along with the limited availability of in-domain DeepMine Farsi training data. We introduce domain-balanced hard prototype mining to finetune the state-of-the-art ECAPA-TDNN x-vector based speaker embedding extractor. The sample mining technique efficiently exploits speaker distances between the speaker prototypes of the popular AAM-softmax loss function to construct challenging training batches that are balanced on the domain-level. To enhance the scoring of cross-lingual trials, we propose a language-dependent s-norm score normalization. The imposter cohort only contains data from the Farsi target-domain which simulates the enrollment data always being Farsi. In case a Gaussian-Backend language model detects the test speaker embedding to contain English, a cross-language compensation offset determined on the AAM-softmax speaker prototypes is subtracted from the maximum expected imposter mean score. A fusion of five systems with minor topological tweaks resulted in a final MinDCF and EER of 0.065 and 1.45% respectively on the SdSVC evaluation set.
11 citations
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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