مقاله انگلیسی رایگان در مورد روش تشخیص چهره نقطه به نقطه با یادگیری هم محور – الزویر ۲۰۲۰

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله یک روش تشخیص چهره نقطه به نقطه با یادگیری هم محور
عنوان انگلیسی مقاله An end-to-end face recognition method with alignment learning
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۱۵ 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)
۲٫۱۱۰ در سال ۲۰۱۹
شاخص H_index ۴۶ در سال ۲۰۲۰
شاخص SJR ۰٫۴۰۴ در سال ۲۰۱۹
شناسه ISSN ۰۰۳۰-۴۰۲۶
شاخص Quartile (چارک) Q3 در سال ۲۰۱۹
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط کامپیوتر
گرایش های مرتبط هوش مصنوعی، مهندسی نرم افزار، معماری سیستم های کامپیوتری
نوع ارائه مقاله
ژورنال
مجله  اپتیک – Optik
دانشگاه School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212003, China
کلمات کلیدی تشخيص چهره، لايه انتقال فضايي، يادگيري هم ترازي، شبكه عصبي پیچشی
کلمات کلیدی انگلیسی Face recognition، Spatial transformation layer، Alignment learning، Convolutional neural network
شناسه دیجیتال – doi
https://doi.org/10.1016/j.ijleo.2020.164238
کد محصول E14716
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- Methodology

۴- Experimental results and analysis

۵- Conclusions

References

بخشی از متن مقاله:

Abstract

Many effective methods have been proposed for face recognition in the past decade and the face recognition accuracy is also gradually improved, but these algorithms usually need to perform face alignment process based on the prior knowledge of facial structure before extracting facial features. The face recognition system usually consists of face detection, face alignment, facial feature extraction, etc., which are independent of each other, and it is difficult to design and train the end-to-end face recognition model. In this paper, an end-to-end face recognition method based on spatial transformation layer is proposed. Specifically, the spatial transformation layer is placed in front of the feature extraction layer of the face recognition network, and the face region is aligned by alignment learning which requires neither prior knowledge nor artificially defined geometric transformation. The face identity category information allows the convolutional neural network to automatically learn the most appropriate face alignment. Simulation experiments on CASIA-WebFace, LFW (Labeled Face in the Wild) and YTF (Youtube Face) face database have shown that the suggested alignment learning algorithm in this paper can realize the end-to-end face recognition and can effectively improve the face recognition rate as well.

Introduction

In the past decade years, the successful application of convolutional neural networks (CNN)in the image field has greatly improved the performance of computer vision tasks, such as face recognition and face verification [1, 2, 3]. The traditional face recognition methods are to construct the classification model mainly with artificially designed features, while the deep learning is to automatically learn more robust facial features through a large amount of training data. Therefore, CNN can achieve better recognition effects in the case of posture, occlusion, and illumination variation [4, 5]. However, the change of face posture is still one challenge of face recognition system in practical application. There are two ways to deal with such problems: one is to create a posture model to handle the facial posture change, Masi et al. [6] proposed an algorithm to calculate the posture distribution of the training data and establish two CNN models which correspond to the frontal face and the profile face respectively, and an excellent face recognition effect was obtained in the case of posture change. Liao et al. [7] suggested a partial face recognition localization method with multi-keypoint descriptors to represent align-free faces in which the descriptors’ size was determined by image content and face images. The other method is to introduce a face alignment process before facial feature extraction. Taigman et al. [8] developed a DeepFace network algorithm using deep learning for face recognition at the first time, and the 3D alignment method was used to solve the problem of out-of-plane rotations that traditional 2D alignment methods could not solve.

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