Face Recognition in Low Quality Images: A Survey

Li, Pei; Prieto, Loreto R.; Mery, Domingo; Flynn, Patrick J.

doi:10.48550/arxiv.1805.11519

Cited by 17 publications

(13 citation statements)

References 135 publications

(137 reference statements)

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“…To evaluate the performance of deblurring methods on real-world images, several no-reference metrics have been used, such as BIQI [80], BLINDS2 [104], BRISQUE [78], CORNIA [147], DIIVINE [81], NIQE [79], and SSEQ [68]. Further, a number of metrics have been developed to evaluate the performance of image deblurring algorithms by comparing the effect on the accuracy of different vision tasks, such as object detection and recognition [64,146].…”

Section: Image Quality Assessmentmentioning

confidence: 99%

Deep Image Deblurring: A Survey

Ren¹,

Luo²,

Lai³

et al. 2022

Preprint

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Image deblurring is a classic problem in lowlevel computer vision, which aims to recover a sharp image from a blurred input image. Recent advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review

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Section: Image Quality Assessmentmentioning

confidence: 99%

Deep Image Deblurring: A Survey

Ren¹,

Luo²,

Lai³

et al. 2022

Preprint

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show abstract

“…On the other hand, image-based data is not unfettered by such predicaments as well. Images face problems like pose and illumination variation, occlusion, and poor image quality [21] [22][23] [24]. Thus, we hypothesize that combining the two modalities and assigning appropriate weights to each of the input streams would bring down the error rate.…”

Section: Problem Statementmentioning

confidence: 99%

DeepMSRF: A Novel Deep Multimodal Speaker Recognition Framework with Feature Selection

Asali

Shenavarmasouleh

Mohammadi

et al. 2021

Transactions on Computational Science and Computational Intelligence

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For recognizing speakers in video streams, significant research studies have been made to obtain a rich machine learning model by extracting high-level speaker's features such as facial expression, emotion, and gender. However, generating such a model is not feasible by using only single modality feature extractors that exploit either audio signals or image frames, extracted from video streams. In this paper, we address this problem from a different perspective and propose an unprecedented multimodality data fusion framework called DeepMSRF, Deep Multimodal Speaker Recognition with Feature selection. We execute DeepMSRF by feeding features of the two modalities, namely speakers' audios and face images. DeepMSRF uses a two-stream VGGNET to train on both modalities to reach a comprehensive model capable of accurately recognizing the speaker's identity. We apply DeepMSRF on a subset of VoxCeleb2 dataset with its metadata merged with VGGFace2 dataset. The goal of DeepMSRF is to identify the gender of the speaker first, and further to recognize his or her name for any given video stream. The experimental results illustrate that DeepMSRF outperforms single modality speaker recognition methods with at least 3 percent accuracy.

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“…Similarly to paper [7], Li et al [5] raised the issue of Low-quality face recognition (LQFR) and classify it into two scenarios: Watch-list identification and Re-identification. In this article, The authors summarized the low-resolution face recognition method into four schemes: Super-resolution reconstruction, Low-quality robust feature, Unified Space, and Deblurring 1 .…”

Section: Introductionmentioning

confidence: 99%

“…The author in the paper[5] also mentioned the de-blurring way. The image quality degradation caused by suspiciousness does not belong to the scope of the low range.…”

mentioning

confidence: 99%

Homogeneous Low-Resolution Face Recognition Method based Correlation Features

Zhao¹

2021

Preprint

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Face recognition technology has been widely adopted in many mission-critical scenarios like means of human identification, controlled admission, and mobile device access, etc. Security surveillance is a typical scenario of face recognition technology. Because the low-resolution feature of surveillance video and images makes it difficult for high-resolution face recognition algorithms to extract effective feature information, Algorithms applied to high-resolution face recognition are difficult to migrate directly to low-resolution situations. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low-resolution surveillance cameras. This paper study on the Correlation Features-based Face Recognition (CoFFaR) method which using for homogeneous low-resolution surveillance videos, the theory, experimental details, and experimental results are elaborated in detail. The experimental results validate the effectiveness of the correlation features method that improves the accuracy of homogeneous face recognition in surveillance security scenarios.

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Face Recognition in Low Quality Images: A Survey

Cited by 17 publications

References 135 publications

Deep Image Deblurring: A Survey

Deep Image Deblurring: A Survey

DeepMSRF: A Novel Deep Multimodal Speaker Recognition Framework with Feature Selection

Homogeneous Low-Resolution Face Recognition Method based Correlation Features

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