Lithology Recognition Method of Core Image Based on Deep Learning

Zhang, Bingxin; Wenguang, Song; Wang, Hao; Xu, Hao

doi:10.26821/ijshre.9.7.2021.9710

Cited by 1 publication

(1 citation statement)

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“…Compared with the traditional face recognition methods, face recognition based on deep learning has a tremendous advantage in recognition accuracy. This shows the importance of deep learning for face recognition [7]. And in the case of difficulties in further upgrading and optimization of network structure, researchers gradually turned their attention to the field of loss function and attention network [8].…”

Section: Introductionmentioning

confidence: 99%

Unrestricted Face Recognition Algorithm Based on Improved Residual Network IR-ResNet-SE

Jing¹,

Jing²,

Lin³

et al. 2023

Journal of Computers

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<p>To solve the problem of poor face recognition performance in unrestricted environments. A face recognition algorithm based on improved residual IR-ResNet-SE is designed. Firstly, the IR structure is added to the 34-layer residual network to reduce the variability of different features; Secondly, we add the channel attention module to increase the weight of important channel features; Finally, the Arcface loss function is used to improve the classification ability of the model. The LFW, AgeDB, and AR datasets reflect unrestricted factors such as pose, age, expression, occlusion, and illumination. The algorithm proposed in this paper is experimented on these three datasets. The experimental results show that the IR-ResNet-SE algorithm proposed in this paper can achieve 99.74% accuracy in the dataset LFW. And it has excellent robustness in face recognition under unrestricted conditions.</p> <p> </p>

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Section: Introductionmentioning

confidence: 99%