scholarly journals Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation

2019 ◽  
Vol 28 (4) ◽  
pp. 2051-2062 ◽  
Author(s):  
Shiming Ge ◽  
Shengwei Zhao ◽  
Chenyu Li ◽  
Jia Li
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
In The Wild ◽  
Knowledge Distillation

scholarly journals Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 146 ◽  
Author(s):  
Vittorio Cuculo ◽  
Alessandro D’Amelio ◽  
Giuliano Grossi ◽  
Raffaella Lanzarotti ◽  
Jianyi Lin
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Single Sample ◽  
Reference Image ◽  
Low Resolution ◽  
Face Expression ◽  
Norm Minimization ◽  
Augmentation Techniques ◽  
In The Wild ◽  
Learned Features

Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative ℓ 0 -norm minimization algorithm called k-LiMapS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8 × 8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations.

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

2019 ◽  
Vol 14 (8) ◽  
pp. 2000-2012 ◽  
Author(s):  
Pei Li ◽  
Loreto Prieto ◽  
Domingo Mery ◽  
Patrick J. Flynn
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
New Techniques ◽  
In The Wild

Caricaturing as a general method to improve poor face recognition: Evidence from low-resolution images, other-race faces, and older adults.

2019 ◽  
Vol 25 (2) ◽  
pp. 256-279 ◽  
Author(s):  
Amy Dawel ◽  
Tsz Ying Wong ◽  
Jodie McMorrow ◽  
Callin Ivanovici ◽  
Xuming He ◽  
...  
Keyword(s):  
Older Adults ◽  
Face Recognition ◽  
Low Resolution ◽  
General Method ◽  
Low Resolution Images

scholarly journals PF -cpGAN: Profile to Frontal Coupled GAN for Face Recognition in the Wild

2020 ◽  
Author(s):  
Fariborz Taherkhani ◽  
Veeru Talreja ◽  
Jeremy Dawson ◽  
Matthew C. Valenti ◽  
Nasser M. Nasrabadi
Keyword(s):  
Face Recognition ◽  
In The Wild

scholarly journals A Study on the Performance of Unconstrained Very Low Resolution Face Recognition: Analyzing Current Trends and New Research Directions

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Luis S. Luevano ◽  
Leonardo Chang ◽  
Heydi Mendez-Vazquez ◽  
Yoanna Martinez-Diaz ◽  
Miguel Gonzalez-Mendoza
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
Research Directions ◽  
Current Trends ◽  
New Research

DDAT: Dual Domain Adaptive Translation for Low-Resolution Face Verification in the Wild

2021 ◽  
pp. 108107
Author(s):  
Qianfen Jiao ◽  
Rui Li ◽  
Wenming Cao ◽  
Jian Zhong ◽  
Si Wu ◽  
...  
Keyword(s):  
Face Verification ◽  
Low Resolution ◽  
In The Wild ◽  
Dual Domain

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

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