Recognition of high-resolution optical vortex modes with deep residual learning

Jingwen, Zhou; Yin, Yin; Tang, Jihong; Ling, Chen; Cao, Meng; Cao, Luping; Liu, Guanhua; Yin, Jianping; Xia, Yong

doi:10.1103/physreva.106.013519

Cited by 15 publications

(7 citation statements)

References 46 publications

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“…(1) Face + + Face + + can achieve accurate and fast face detection, comparison, search and attribute analysis [4]. In addition, Face + + also has the function of face attribute analysis, which can analyze the age, gender, and emotion of the face(figure 2).…”

Section: Face Recognition Methods Based On Large-scale Datamentioning

confidence: 99%

A review of face recognition technology based on deep learning

Gao

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Face recognition technology is a kind of biometric recognition technology that has developed rapidly in recent years, and the method based on deep learning is one of the most promising research directions. In this paper, the face recognition technology based on deep learning is reviewed and analyzed. Firstly, the mainstream data sets of face recognition technology are introduced. Then, two kinds of face recognition methods based on large-scale data and synthetic data are analyzed and verified by experiments. Finally, the advantages and challenges of the face recognition method based on deep learning are discussed, and some hot spots and future development directions in current research are prospected. However, face recognition technology based on deep learning has higher accuracy and robustness. At the same time, there are still problems such as unbalanced data sets, responses in complex scenarios, and data privacy protection. This paper provides important reference and guidance for researchers by systematically combing and summarizing the research progress and application in this field and then promotes the development and application of face recognition technology.

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Section: Face Recognition Methods Based On Large-scale Datamentioning

confidence: 99%

A review of face recognition technology based on deep learning

Gao

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…Taigman et al (2014) achieved a good recognition effect by constructing a neural network with 120 million parameters and using large-scale data for training. Zhou et al (2015) proposed a ten-layer simple convolutional neural network to further increase the accuracy. The DeepId series (Sun et al, 2014a(Sun et al, ,b, 2015a improves the classic neural network structure of image classification and applies it to face recognition to improve performance.…”

Section: Related Workmentioning

confidence: 99%

DCM2Net: an improved face recognition model for panoramic stereoscopic videos

Zhang,

Tan,

Wei

et al. 2024

Front. Artif. Intell.

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The panoramic stereo video has brought a new visual experience for the audience with its immersion and stereo effect. In panoramic stereo video, the face is an important element. However, the face image in panoramic stereo video has varying degrees of deformation. This brings new challenges to face recognition. Therefore, this paper proposes a face recognition model DCM2Net (Deformable Convolution MobileFaceNet) for panoramic stereo video. The model mainly integrates the feature information between channels during feature fusion, redistributes the information between channels in the deeper part of the network, and fully uses the information between different channels for feature extraction. This paper also built a panoramic stereo video live system, using the DCM2Net model to recognize the face in panoramic stereo video, and the recognition results are displayed in the video. After experiments on different datasets, the results show that our model has better results on popular datasets and panoramic datasets.

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“…Из-за большого разнообразия входных изображений одного и того же класса становится трудно правильно их классифицировать за счет слишком большой разницы с эталонными изображениями. Экспериментально было замечено, что дескрипторы таких изображений даже для различных людей, например, детей или пожилых людей различных рас [13], обычно близки по расстоянию друг к другу. Таким образом, основная задача настоящей статьи состоит в том, чтобы автоматически проверить, является ли входное изображение редким / аномальным и может ли его распознавание привести к ухудшению итогового качества.…”

Section: постановка задачиunclassified

Open-set face identification with automatic detection of out-of-distribution images

Sokolova¹,

Savchenko²,

Nikolenko³

2022

Computer Optics

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One of main issues in face identification is the lack of training data of specific type (bad quality image, varying scale or illumination, children/old people faces, etc.). As a result, the recogni-tion accuracy may be low for input images which are not similar to the majority of images in the dataset used to train the feature extractor. In this paper, we propose that this issue is dealt with by the automatic detection of such out-of-distribution data based on the addition of a preliminary stage of their automatic rejection using a special convolutional network trained using a set of rare data collected using various transformations. To increase the computational efficiency, the decision about the presence of a rare image is made on the basis of the same face descriptor that is used in the classifier. Experimental research confirmed the accuracy improvement of the proposed approach for several datasets of faces and modern neural network descriptors.

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Recognition of high-resolution optical vortex modes with deep residual learning

Cited by 15 publications

References 46 publications

A review of face recognition technology based on deep learning

A review of face recognition technology based on deep learning

DCM2Net: an improved face recognition model for panoramic stereoscopic videos

Open-set face identification with automatic detection of out-of-distribution images

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