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: The proposed RRCSL method seeks a linear transformation to enlarge the cosine similarity of intra-class and reduce the cosin similarity of inter-class as much as possible, and adaptively learns the norm of samples to the scaled circle by exploiting the ring regularization term simultaneously.
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TL;DR: In this article, the authors used machine learning and deep learning to predict nano-bio interactions, but such a prediction is now hindered by the paucity of suitable nanodescripts.
Abstract: Artificial intelligence approaches, such as machine learning and deep learning, may predict nano–bio interactions. However, such a prediction is now hindered by the paucity of suitable nanodescript...
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TL;DR: Experimental results demonstrate that the proposed cloud-based face video retrieval system with deep learning outperforms in terms of recognition accuracy and computational time.
Abstract: Face video retrieval is an attractive research topic in computer vision. However, it remains challenges to overcome because of the significant variation in pose changes, illumination conditions, occlusions, and facial expressions. In video content analysis, face recognition has been playing a vital role. Besides, deep neural networks are being actively studied, and deep learning models have been widely used for object detection, especially for face recognition. Therefore, this study proposes a cloud-based face video retrieval system with deep learning. First, a dataset is collected and pre-processed. To produce a useful dataset for the CNN models, blurry images are removed, and face alignment is implemented on the remaining images. Then the final dataset is constructed and used to pre-train the CNN models (VGGFace, ArcFace, and FaceNet) for face recognition. We compare the results of these three models and choose the most efficient one to develop the system. To implement a query, users can type in the name of a person. If the system detects a new person, it performs enrolling that person. Finally, the result is a list of images and time associated with those images. In addition, a system prototype is implemented to verify the feasibility of the proposed system. Experimental results demonstrate that this system outperforms in terms of recognition accuracy and computational time.
19 citations
TL;DR: The proposed generalized illumination robust (GIR) model is integrated with the pre-trained deep learning (PDL) model to construct the GIR-PDL model and the experimental results indicate that the proposed methods are efficient to tackle severe illumination variations.
Abstract: This paper models the driver face recognition problem under the intelligent traffic monitoring systems as severe illumination variation face recognition with single sample problem. Firstly, in the point of view of numerical value sign, the current illumination invariant unit is derived from the subtraction of two pixels in the face local region, which may be positive or negative, we propose a generalized illumination robust (GIR) model based on positive and negative illumination invariant units to tackle severe illumination variations. Then, the GIR model can be used to generate several GIR images based on the local edge-region or the local block-region, which results in the edge-region based GIR (EGIR) image or the block-region based GIR (BGIR) image. For single GIR image based classification, the GIR image utilizes the saturation function and the nearest neighbor classifier, which can develop EGIR-face and BGIR-face. For multi GIR images based classification, the GIR images employ the extended sparse representation classification (ESRC) as the classifier that can form the EGIR image based classification (GIRC) and the BGIR image based classification (BGIRC). Further, the GIR model is integrated with the pre-trained deep learning (PDL) model to construct the GIR-PDL model. Finally, the performances of the proposed methods are verified on the Extended Yale B, CMU PIE, AR, self-built Driver and VGGFace2 face databases. The experimental results indicate that the proposed methods are efficient to tackle severe illumination variations.
19 citations
Cites background or methods from "ArcFace: Additive Angular Margin Lo..."
...deep learning based approach [28]–[31] is the best to learn...
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...or ArcFace [31] performs unsatisfactorily under severe illumi-...
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...3) Illumination Invariant Measure: Gradient-face [15], Weber-face [31], MSLDE [34], LNN-face [14], MSLDE + ESRC and LNN-face + ESRC....
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...4) Pre-Trained Deep Learning Model: VGG [29] and ArcFace [31], VGG/ArcFace + ESRC....
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...learning models VGG [29] and ArcFace [31] are adopted....
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06 May 2021
TL;DR: In this article, the suitability of synthetic face images generated with StyleGAN and StyleGAN2 to compensate for the urgent lack of publicly available largescale test data was investigated for the European Entry/Exit System, which integrates face recognition mechanisms.
Abstract: Face verification has come into increasing focus in various applications including the European Entry/Exit System, which integrates face recognition mechanisms. At the same time, the rapid advancement of biometric authentication requires extensive performance tests in order to inhibit the discriminatory treatment of travellers due to their demographic background. However, the use of face images collected as part of border controls is restricted by the European General Data Protection Law to be processed for no other reason than its original purpose. Therefore, this paper investigates the suitability of synthetic face images generated with StyleGAN and StyleGAN2 to compensate for the urgent lack of publicly available largescale test data. Specifically, two deep learning-based (SER-FIQ, FaceQnet v1) and one standard-based (ISO/IEC TR 29794-5) face image quality assessment algorithm is utilized to compare the applicability of synthetic face images compared to real face images extracted from the FRGC dataset. Finally, based on the analysis of impostor score distributions and utility score distributions, our experiments reveal negligible differences between StyleGAN vs. StyleGAN2, and further also minor discrepancies compared to real face images.
19 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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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