مقاله انگلیسی رایگان در مورد نمایش اسپارس تحت نظارت قوی برای تشخیص چهره – الزویر ۲۰۲۰

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله نمایش اسپارس تحت نظارت قوی برای تشخیص چهره
عنوان انگلیسی مقاله Robust supervised sparse representation for face recognition
انتشار  مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۳۵ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۲٫۰۲۶ در سال ۲۰۱۹
شاخص H_index ۴۱ در سال ۲۰۲۰
شاخص SJR ۰٫۲۹۱ در سال ۲۰۱۹
شناسه ISSN ۱۳۸۹-۰۴۱۷
شاخص Quartile (چارک) Q4 در سال ۲۰۱۹
مدل مفهومی دارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط کامپیوتر
گرایش های مرتبط مهندسی الگوریتم ها و محاسبات، هوش مصنوعی، مهندسی نرم افزار
نوع ارائه مقاله
ژورنال
مجله  تحقیقات سیستم های شناختی – Cognitive Systems Research
دانشگاه Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
کلمات کلیدی تشخیص چهره، تجزیه Huber، نمایش اسپارس نظارت شده
کلمات کلیدی انگلیسی Face recognition، Huber Loss، Supervised sparse representation
شناسه دیجیتال – doi
https://doi.org/10.1016/j.cogsys.2020.02.001
کد محصول E14721
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 works

۳- Robust coding based supervised sparse representation

۴- Sparsity and robustness of our model

۵- Experiment results

۶- Conclusions

References

بخشی از متن مقاله:

Abstract

Sparse representation based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum -norm on coding coefficient vector, which is considered as an unsupervised sparsity constraint and usually requires high computational cost. On the other hand, supervised sparsity representation based method (SSR) realizes sparse representation classification with higher efficiency by multiple phases of representing a probe. Nevertheless, since previous SSR methods only deal with Gaussian noise, they cannot satisfy empirical face recognition application which faces wide variations. In this paper, we propose a robust supervised sparse representation (RSSR) model, which uses two-phase of robust representation to compute a sparse coding vector. Huber loss is employed as the fidelity term in the linear representation, which improves the competitiveness of correct class in the first phase. Then training samples with weak competitiveness are removed by supervised way. In the second phase, the competitiveness of correct class is further boosted by Huber loss. We compare the RSSR with other state-of-the-art methods under different conditions, including illumination variations, gesture changes, expressions, corruptions, and occlusions. Comprehensive experiments on four open databases demonstrate the robustness of RSSR and competitive performance is obtained in dealing with face images with occlusion or not.

introduction

Using biometric identification technology for verifying identity is gaining more importance, and different techniques have been developed such as Palmprint recognition[1][2] and face recognition(FR)[3][4]. FR[5][6][7]has been extensively studied for its broad application prospects in recent years, such as authentication and payment system. The primary task of FR consists of feature extraction and classification[8][9][10]. For many classifiers, feature extraction that tends to discover discriminative feature is very important, which has great influence on recognition rate. Since there is rich redundancy in a face image, low dimensional features are extracted to concisely represent the samples in training set[11][12][13][14]. So that using these features can alleviate the computational cost and improve the recognition performance of classifiers. For empirical applications of FR, various changes including lighting, expression, pose, and occlusion can be seen in a probe and which could not included in training set, which leads to the consequence that the computed feature becomes inefficient. Image segmentation can be used to help the effective representation of an image by retaining the informative parts of images[15][16].

 

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