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 semi-supervised performance evaluation for face recognition (SPE-FR) method is proposed, which is based on parametric Bayesian modeling of the face embedding similarity scores.
Abstract: AbstractWe introduce Semi-supervised Performance Evaluation for Face Recognition (SPE-FR). SPE-FR is a statistical method for evaluating the performance and algorithmic bias of face verification systems when identity labels are unavailable or incomplete. The method is based on parametric Bayesian modeling of the face embedding similarity scores. SPE-FR produces point estimates, performance curves, and confidence bands that reflect uncertainty in the estimation procedure. Focusing on the unsupervised setting wherein no identity labels are available, we validate our method through experiments on a wide range of face embedding models and two publicly available evaluation datasets. Experiments show that SPE-FR can accurately assess performance on data with no identity labels, and confidently reveal demographic biases in system performance.KeywordsAlgorithmic biasSemi-supervised evaluationFace verificationBayesian inference
4 citations
29 May 2019
TL;DR: This study asks 26 participants to provide subjective quality scores that represent the ease of recognizing the face on the images from a smartphone based face image dataset and observes that the subjective scores outperform the implemented objective scores while having a low correlation with them.
Abstract: The performance of any face recognition system gets affected by the quality of the probe and the reference images. Rejecting or recapturing images with low-quality can improve the overall performance of the biometric system. There are many statistical as well as learning-based methods that provide quality scores given an image for the task of face recognition. In this study, we take a different approach by asking 26 participants to provide subjective quality scores that represent the ease of recognizing the face on the images from a smartphone based face image dataset. These scores are then compared to measures implemented from ISO/IEC TR 29794-5. We observe that the subjective scores outperform the implemented objective scores while having a low correlation with them. Furthermore, we analyze the effect of pose, illumination, and distance on face recognition similarity scores as well as the generated mean opinion scores.
4 citations
Cites methods from "ArcFace: Additive Angular Margin Lo..."
...To calculate the biometric scores, the ArcFace[4] deep learning method is used to extract embeddings representing each face image....
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TL;DR: The purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.
Abstract: Recognizing Families In the Wild (RFIW): an annual large-scale, multi-track automatic kinship recognition evaluation that supports various visual kin-based problems on scales much higher than ever before. Organized in conjunction with the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG) as a Challenge, RFIW provides a platform for publishing original work and the gathering of experts for a discussion of the next steps. This paper summarizes the supported tasks (i.e., kinship verification, tri-subject verification, and search & retrieval of missing children) in the evaluation protocols, which include the practical motivation, technical background, data splits, metrics, and benchmark results. Furthermore, top submissions (i.e., leader-board stats) are listed and reviewed as a high-level analysis on the state of the problem. In the end, the purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.
4 citations
Cites methods from "ArcFace: Additive Angular Margin Lo..."
...th FaceNet (Inception-ResNet-v1) and with VGG-Face (Resnet-50) as the pre-trained models. FaceNet uses Triplet Loss as the main loss function in the training phase. The authors also implement ArcFace [2] - a family of loss functions based on the geodesic distance between feature vectors which aim to discriminate the latent representation of deep NNs. V. DISCUSSION A. A Broader Impact The fourth Recog...
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TL;DR: In this article , a hierarchical pseudo-3D convolution neural network based on a kernel attention mechanism with a new global context block is proposed to accurately predict Alzheimer's disease even when the input features are complex.
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TL;DR: This article used representational similarity analysis to investigate how well the representations learned by high-performing deep convolutional neural networks (DCNNs) match human brain representations across the entire distributed face processing system and found that the correspondence between intermediate layers and neural representations of naturalistic human face processing is weak at best, and diverges even further in the later fully connected layers.
Abstract: Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The relationships between internal representations learned by DCNNs and those of the primate face processing system are not well understood, especially in naturalistic settings. We developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces) and used representational similarity analysis to investigate how well the representations learned by high-performing DCNNs match human brain representations across the entire distributed face processing system. DCNN representational geometries were strikingly consistent across diverse architectures and captured meaningful variance among faces. Similarly, representational geometries throughout the human face network were highly consistent across subjects. Nonetheless, correlations between DCNN and neural representations were very weak overall--DCNNs captured 3% of variance in the neural representational geometries at best. Intermediate DCNN layers better matched visual and face-selective cortices than the final fully-connected layers. Behavioral ratings of face similarity were highly correlated with intermediate layers of DCNNs, but also failed to capture representational geometry in the human brain. Our results suggest that the correspondence between intermediate DCNN layers and neural representations of naturalistic human face processing is weak at best, and diverges even further in the later fully-connected layers. This poor correspondence can be attributed, at least in part, to the dynamic and cognitive information that plays an essential role in human face processing but is not modeled by DCNNs. These mismatches indicate that current DCNNs have limited validity as in silico models of dynamic, naturalistic face processing in humans.
4 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.
Abstract: TensorFlow is an interface for expressing machine learning algorithms, and an
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TensorFlow can be executed with little or no change on a wide variety of
heterogeneous systems, ranging from mobile devices such as phones and tablets
up to large-scale distributed systems of hundreds of machines and thousands of
computational devices such as GPU cards. The system is flexible and can be used
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Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
10,447 citations