یادگیری ویژگی نمایش مبتنی بر پچ چند مقیاسی
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یادگیری ویژگی نمایش مبتنی بر پچ چند مقیاسی

عنوان فارسی مقاله: یادگیری ویژگی نمایش مبتنی بر پچ چند مقیاسی برای تشخیص چهره با وضوح پایین
عنوان انگلیسی مقاله: Multi-scale patch based representation feature learning for low-resolution face recognition
مجله/کنفرانس: محاسبات نرم کاربردی - Applied Soft Computing
رشته های تحصیلی مرتبط: کامپیوتر
گرایش های تحصیلی مرتبط: هوش مصنوعی، مهندسی نرم افزار
کلمات کلیدی فارسی: تشخیص چهره، وضوح پایین، یادگیری ویژگی، پچ چند مقیاسی
کلمات کلیدی انگلیسی: Face recognition، Low-resolution، Feature learning، Multi-scale patch
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.asoc.2020.106183
دانشگاه: Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 6/031 در سال 2019
شاخص H_index: 110 در سال 2020
شاخص SJR: 1/216 در سال 2019
شناسه ISSN: 1568-4946
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 20
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: E14717
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- The proposed MSPRFL


3- Experiments and discussions


4- Conclusions


References

نمونه متن انگلیسی مقاله

Abstract


In practical video surveillance, the quality of facial regions of interest is usually affected by the large distances between the objects and surveillance cameras, which undoubtedly degrade the recognition performance. Existing methods usually consider the holistic representations, while neglecting the complementary information from different patch scales. To tackle this problem, this paper proposes a multi-scale patch based representation feature learning (MSPRFL) scheme for low-resolution face recognition problem. Specifically, the proposed MSPRFL approach first exploits multi-level information to learn more accurate resolution-robust representation features of each patch with the help of a training dataset. Then, we exploit these learned resolution-robust representation features to reduce the resolution discrepancy by integrating the recognition results from all patches. Finally, by considering the complementary discriminative ability from different patch scales, we try to fuse the multi-scale outputs by learning scale weights via an ensemble optimization model. We further verify the efficiency of the proposed MSPRFL on low-resolution face recognition by the comparison experiments on several commonly used face datasets.


Introduction


Face image recognition, as one of the most commonly used biometrics technologies, has become the research hotspot of the pattern recognition community in past decades [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Generally, most of the current methods perform well on the cases that the acquired region of interest (ROI) has high image resolution and contains enough discriminative information for recognition tasks. However, in real-world robotics and video surveillance applications, the observed faces generally have low-resolution (LR) together with pose and illumination variations, while the referenced faces are always enrolled with high resolution (HR). The main challenge is to match an LR probe face with limited details against HR gallery faces. We name this kind of problem as low-resolution face recognition (LRFR). An alternative solution is down-sampling the HR galleries and then matching in the same resolution space. In this way, the resolution discrepancy is reduced at the expense of losing the discriminative facial details in the gallery. Generally, there are two typical categories to address the LRFR problem. One is superresolution approaches, which first synthesize the target HR faces from the observed LR image, and then utilize traditional face recognition approaches in the common resolution domain. The other is resolution-robust feature extraction methods, which directly extract discriminative features from respective domains, thus obtain better performance than superresolution methods.

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