Low-resolution face recognition via convolutional neural network

Ding, Chengbiao; Bao, Tao; Karmoshi, Saleem; Zhu, Ming

doi:10.1109/iccsn.2017.8230292

Cited by 12 publications

(14 citation statements)

References 20 publications

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“…Based on the literature review result on Table I, the goal of the research is to increase the recognition rate. Our research focus is on CNN method and the model in [9] is the state-ofthe-art of our research. We found that the state-of-the-art use Bicubic for the preprocessing phase.…”

Section: Related Workmentioning

confidence: 99%

“…The input is from low-resolution video face recognition with a manifold-based track comparison strategy. In the next year in 2017 [9] proposed a multi-resolution convolutional neural network (MRCNN). This model is proposed in order to study the consistent feature representation from high-resolution and low-resolution face images.…”

Section: Introductionmentioning

confidence: 99%

“…The main problem of this research is a low-resolution image as the input process. The state-of-the-art method [9] works on feature extraction phase, and Bicubic as their preprocessing phase. Their experiment result showed great value in recognition rate.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, MRCNN [9] method is proposed to achieve better accuracy of face recognition of low-resolution image. Anti-alias [11] is used to generate a high resolution (HR) image from low resolution (LR) probe and MRCNN is carefully designed to learn the consistent feature representations of both the gallery images and the generated ones.…”

Section: Introductionmentioning

confidence: 99%

“…Anti-alias [11] is used to generate a high resolution (HR) image from low resolution (LR) probe and MRCNN is carefully designed to learn the consistent feature representations of both the gallery images and the generated ones. This paper uses Anti-alias, Anti-alias produce better image rather than Bicubic that is used in state of the art [9].…”

Section: Introductionmentioning

confidence: 99%

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Face Recognition on Low-Resolution Image using Multi Resolution Convolution Neural Network and Antialiasing Method

Imandito¹,

Suharjito²

2019

IJACSA

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Video surveillance applications usually take pictures of faces that have a low resolution (12x12) due to distance, lighting and shooting angles Most of face recognition algorithm have the poor performance accuracy and poor identify face on low resolution. Based on the problem, identifying the face of the query in low resolution, based on high resolution (64x64) proves to be a huge challenge. The aim of this research is to develop a new model for face recognition of low-resolution image in order to increase the accuracy of recognition. A Multi-Resolution Convolutional Neural Network (MRCNN) is proposed to address the problem. First, Antialiasing is used in preprocessing phase, then use MRCNN to extract the feature of the image. LWF (Labeled Face in Wild) will be used to evaluate the model. The result of this study is increasing the accuracy of face recognition on low-resolution image compared to the previous MRCNN model.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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