نمایش اسپارس تحت نظارت قوی برای تشخیص چهره
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نمایش اسپارس تحت نظارت قوی برای تشخیص چهره

عنوان فارسی مقاله: نمایش اسپارس تحت نظارت قوی برای تشخیص چهره
عنوان انگلیسی مقاله: Robust supervised sparse representation for face recognition
مجله/کنفرانس: تحقیقات سیستم های شناختی - Cognitive Systems Research
رشته های تحصیلی مرتبط: کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، هوش مصنوعی، مهندسی نرم افزار
کلمات کلیدی فارسی: تشخیص چهره، تجزیه Huber، نمایش اسپارس نظارت شده
کلمات کلیدی انگلیسی: Face recognition، Huber Loss، Supervised sparse representation
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.cogsys.2020.02.001
دانشگاه: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 2/026 در سال 2019
شاخص H_index: 41 در سال 2020
شاخص SJR: 0/291 در سال 2019
شناسه ISSN: 1389-0417
شاخص Quartile (چارک): Q4 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 35
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14721
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related works


3- Robust coding based supervised sparse representation


4- Sparsity and robustness of our model


5- Experiment results


6- Conclusions


References

نمونه متن انگلیسی 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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