Low-resolution face recognition with single sample per person

Chu, Yongjie; Ahmad, Touqeer; Bebis, George; Zhao, Lindu

doi:10.1016/j.sigpro.2017.05.012

Cited by 33 publications

(14 citation statements)

References 28 publications

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“…We follow the experiment setting provided in [73] and [59] and study both closed-set and open-set scenarios. For closed-set evaluation, 180 subjects are used and for open set evaluation, we compare our result with the performance reported in [59] with the HD-1m HD-2.6m HD-4.7m SCface [14] 6.18% 6.18% 1.82% CLPM [35] 3.08% 4.32% 3.46% SSR [82] 18.09% 13.2% 7.04% CSCDN [75] 18.97% 13.58% 6.99% CCA [76] 20.69% 14.85% 9.79% DCA [16] 25.53% 18.44% 12.19% C-RSDA [11] 18.46% 18.08% 15.77% Centerloss [77] [35] 29.12% SDA [91] 40.08% CMFA [64] 39.56% Coupled mapping method [63] 43.24% LMCM [87] 60.40% Centerloss [77] D. Low-resolution face re-identification In this section, we explore LR face re-identification and evaluate it on several datasets captured in an unconstrained environment. We employ the VBOLO dataset for an in-depth study and the SCface, UCCSface, and MegaFace challenge 2 LR subset for other topical explorations.…”

Section: Low-resolution Face Identificationmentioning

confidence: 99%

On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

Prieto

Mery

et al. 2019

IEEE Trans.Inform.Forensic Secur.

140

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Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition task remains challenging, especially when the lowresolution faces are captured under non-ideal conditions, as is common in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, non-uniform lighting, and non-frontal face pose. In this paper, we analyze face recognition techniques using data captured under lowquality conditions in the wild. We provide a comprehensive analysis of experimental results for two of the most important applications in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance. The following three contributions are made: (i) we conduct experiments to evaluate super-resolution methods for low-resolution face recognition; (ii) we study face re-identification on various public face datasets including real surveillance and low-resolution subsets of large-scale datasets, present a baseline result for several deep learning based approaches, and improve them by introducing a Generative Adversarial Network (GAN) pre-training approach and fully convolutional architecture; and (iii) we explore low-resolution face identification by employing a state-of-the-art supervised discriminative learning approach. Evaluations are conducted on challenging portions of the SCface and UCCSface datasets.

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Section: Low-resolution Face Identificationmentioning

confidence: 99%

On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

Prieto

Mery

et al. 2019

IEEE Trans.Inform.Forensic Secur.

140

View full text Add to dashboard Cite

show abstract

“…Next research in 2017 [12] proposed a cluster-based regularized simultaneous discriminant analysis (C-RSDA) based on SDA. Next year in 2018 [13] proposed a method called low-rank representation and locality-constrained regression (LLRLCR) to learn occlusion-robust representation features.…”

Section: Related Workmentioning

confidence: 99%

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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“…Hence, the identification of a human face may imply the recognition of the invariant structure of aspects in dynamic environments of our daily life [ 13 ]. The most ecological environments will be related to low viewing conditions [ 28 ], in terms of lighting [ 29 ] or distance [ 30 ], among others. One of the most interesting variants that can include all the variables described above is the movement or the position of presentation and a face.…”

Section: Introductionmentioning

confidence: 99%

Profiles on the Orientation Discrimination Processing of Human Faces

Moret-Tatay

Baixauli-Fortea

Grau-Sevilla

2020

IJERPH

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Face recognition is a crucial subject for public health, as socialization is one of the main characteristics for full citizenship. However, good recognizers would be distinguished, not only by the number of faces they discriminate but also by the number of rejected stimuli as unfamiliar. When it comes to face recognition, it is important to remember that position, to some extent, would not entail a high cognitive cost, unlike other processes in similar areas of the brain. The aim of this paper was to examine participant’s recognition profiles according to face position. For this reason, a recognition task was carried out by employing the Karolinska Directed Emotional Faces. Reaction times and accuracy were employed as dependent variables and a cluster analysis was carried out. A total of two profiles were identified in participants’ performance, which differ in position in terms of reaction times but not accuracy. The results can be described as follows: first, it is possible to identify performance profiles in visual recognition of faces that differ in position in terms of reaction times, not accuracy; secondly, results suggest a bias towards the left. At the applied level, this could be of interest with a view to conducting training programs in face recognition.

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Low-resolution face recognition with single sample per person

Cited by 33 publications

References 28 publications

On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

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

Profiles on the Orientation Discrimination Processing of Human Faces

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