DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Abstract
In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.
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Pointwise group convolutions prevent feature maps between different groups from communicating with each other, which is harmful to extract representative feature maps.
“However, one problem is that pointwise group convolutions prevent feature maps between different groups from communicating with each other, which is harmful to extract representative feature maps.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [49]
ShuffleNet v1 was proposed to construct a high-efficient CNN structure for resource-limited devices, with two innovations: pointwise group convolution and channel shuffle.
“ShuffleNet v1 [49] was proposed to construct a high-efficient CNN structure for resource-limited devices.There are two innovations: pointwise group convolution and channel shuffle.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [49]
The authors of ShuffleNet v1 reckon that Xception and ResNeXt are less efficient in extremely small networks since 1×1 convolution requires a lot of computing resources; therefore, pointwise group convolution is proposed to reduce the computation complexity of 1×1 convolutions.
“The authors of ShuffleNet v1 reckon that Xception [26] and ResNeXt [29] are less efficient in extremely small networks since 1 × 1 convolution requires a lot of computing resources.Therefore, pointwise group convolution is proposed to reduce the computation complexity of 1 × 1 convolutions.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [49]
There are two versions of ShuffleNets: ShuffleNet v1 and ShuffleNet v2.
“So far, there are two versions of ShuffleNets, namely ShuffleNet v1 [49] and ShuffleNet v2 [50].”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [49]
ShuffleNets are a series of CNN-based models proposed by MEGVII to solve the problem of insufficient computing power of mobile devices; they combine pointwise group convolution, channel shuffle, and some other techniques, which significantly reduce computational cost with little loss of accuracy.
“ShuffleNets are a series of CNN-based models proposed by MEGVII to solve the problem of insufficient computing power of mobile devices.These models combine pointwise group convolution, channel shuffle, and some other techniques, which significantly reduce the computational cost with little loss of accuracy.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [49]
Pointwise group convolution requires each convolution operation is only on the corresponding input channel group, which can reduce the computational complexity.
“Pointwise group convolution, shown in Fig. 17 (a), requires each convolution operation is only on the corresponding input channel group, which can reduce the computational complexity.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [49]
Channel shuffle operation is inserted to promote information exchanges between groups.
“Therefore, channel shuffle operation, shown in Fig. 17 Fig.17.Pointwise group convolution and channel shuffle in ShuffleNet v1 (a) Pointwise group convolution where different colors represent different groups.(b) After acquiring feature 1, channel shuffle operation is inserted to promote information exchanges between groups”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [49]
Deep neural networks have been widely used and achieved excellent performance in many applications including speech recognition, face recognition, image classification, object detection, semantic segmentation, and natural language processing.
“Deep neural networks (DNNs) have been widely used in many areas and have achieved excellent performance in many applications including speech recognition [24], face recognition [25], image classification [26], object detection [27], semantic segmentation [28], natural language processing [29]”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [25]
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- Authors
- Taigman, Y., Yang, M., Ranzato, M., & Wolf, L.
- Affiliations
- Meta (United States) (US)
- Tel Aviv University (IL)
- ORCID
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 1701-1708
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2145287260
- Cited by
- 6,542
- References
- 1
- Citations by year
- 2026: 166 · 2025: 358 · 2024: 394 · 2023: 486 · 2022: 528 · 2021: 729 · 2020: 741 · 2019: 818 · 2018: 737 · 2017: 670 · 2016: 480 · 2015: 371 · 2014: 60 · 2012: 1
- FWCI
- 283.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- face verification, face recognition, 3D face modeling, deep neural network, unconstrained face recognition, Labeled Faces in the Wild
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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