مقاله انگلیسی رایگان در مورد شبکه عمیق مشترک طبقه بند برای تشخیص چهره با وضوح پایین – الزویر ۲۰۲۰

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله شبکه عمیق مشترک طبقه بند با اتلاف چند سلسله مراتبی برای تشخیص چهره با وضوح پایین
عنوان انگلیسی مقاله Classifier shared deep network with multi-hierarchy loss for low resolution face recognition
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۲۸ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۳٫۸۰۹ در سال ۲۰۱۹
شاخص H_index ۷۲ در سال ۲۰۲۰
شاخص SJR ۰٫۵۶۲ در سال ۲۰۱۹
شناسه ISSN ۰۹۲۳-۵۹۶۵
شاخص Quartile (چارک) Q2 در سال ۲۰۱۹
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط کامپیوتر
گرایش های مرتبط هوش مصنوعی، مهندسی الگوریتم ها و محاسبات، مهندسی نرم افزار
نوع ارائه مقاله
ژورنال
مجله  پردازش سیگنال. ارتباط تصویر – Signal Processing. Image Communication
دانشگاه Shenzhen Key Lab. of Info. Sci&Tech/Shenzhen Engineering Lab. of IS&DCP, Department of Electronic Engineering/Graduate School at Shenzhen, Tsinghua University, China
کلمات کلیدی تشخیص چهره با وضوح پایین، شبکه عمیق مشترک دسته بند، از دست دادن چند سلسله مراتبی، ویژگی های واسط
کلمات کلیدی انگلیسی Low-resolution face recognition، Classifier shared deep network، Multi-hierarchy loss، Intermediate features
شناسه دیجیتال – doi
https://doi.org/10.1016/j.image.2019.115766
کد محصول E14825
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract

۱- Introduction

۲- Related work

۳- The proposed method

۴- Experiments

۵- Conclusions

References

بخشی از متن مقاله:

Abstract

Face images in real Closed-Circuit Television (CCTV) are usually with low resolution, which remarkably deteriorates the performance of existing face recognition algorithms and hinders the application of face recognition. The main technical focus of this issue, matching between high-resolution (HR) and low-resolution (LR) face images has attracted significant attention. In order to better address this problem, we propose a Classifier Shared Deep Network with Multi-Hierarchy Loss (CS-MHL-Net) for low-resolution face recognition (LRFR) in this paper. Firstly, considering that contrastive loss and its variants are not conducive to the convergence of network and the reduction of discrepancy, a shared classifier between HR and LR face images is proposed to further narrow the domain gap between HR and LR by sharing the corresponding weights which can be seen as the class center. Secondly, to fully exploit intermediate features and loss constraints, we embed multi-hierarchy loss into intermediate layers, with the target of reducing the distances between HR and LR intermediate features after max pooling and avoiding the decreasing of accuracy caused by over-utilization of intermediate features. Experimental results on LFW and SCface demonstrate the effectiveness and superiority of the proposed method.

Introduction

Convolutional neural networks (CNNs) have achieved great success in many fields such as object classification [1, 2], scene understanding [3, 4], and action recognition [5]. Most importantly, CNNs have greatly improved the perfor mance of face recognition [6, 7, 8, 9] in recent years, which laid the foundation for face recognition in real applications. Current accuracy of the-state-ofthe-art face recognition algorithms has achieved more than 99% on the LFW database [10]. However, in reality, the qualities of images captured by surveillance videos are severely affected by different image resolutions. The recognition accuracy dropped severely when identifying extremely low-resolution images. In this paper, we will focus on improving the performance of low-resolution face recognition (LRFR) which has made progress and many more [11, 12, 13, 14, 15, 16, 17, 18]. This paper focuses on the matching problem between low-resolution (LR) face images and high-resolution (HR) face images. How to make the network extracting discriminative features of LR face images and narrowing the domain gap between HR and LR are the main directions to improve the performance of LRFR. There are many traditional works [14, 19, 20, 13, 21, 22, 23, 24, 18, 15, 25, 26] making contributions to the improvement of LRFR. Some of these works [14, 13, 21, 19] focus on transforming the LR images to HR images and promoting the recognition accuracy through the reconstructed LR images. The other works [19, 20, 22, 23, 24, 18, 15, 25, 26] pay more attention to the process of extracting the LR features and narrow the distances between LR features and HR features.

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