Low resolution face recognition using Mixture of Experts with different representations

2011 ◽  
Author(s):  
Reza Ebrahimpour ◽  
Naser Sadeghnejad ◽  
Seyed Ali Asghar Abbaszadeh Arani ◽  
Ali Amiri
Keyword(s):  
Face Recognition ◽  
Mixture Of Experts ◽  
Low Resolution

Boosted Pre-loaded Mixture of Experts for low-resolution face recognition

2012 ◽  
Vol 9 (3) ◽  
pp. 145-158 ◽  
Author(s):  
Reza Ebrahimpour ◽  
Naser Sadeghnejad ◽  
Saeed Masoudnia ◽  
Seyed Ali Asghar Abbaszadeh Arani
Keyword(s):  
Face Recognition ◽  
Mixture Of Experts ◽  
Low Resolution

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 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

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.

Efficient Low-Resolution Face Recognition via Bridge Distillation

2020 ◽  
Vol 29 ◽  
pp. 6898-6908
Author(s):  
Shiming Ge ◽  
Shengwei Zhao ◽  
Chenyu Li ◽  
Yu Zhang ◽  
Jia Li
Keyword(s):  
Face Recognition ◽  
Low Resolution

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

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
High Resolution ◽  
Domain Adaptation ◽  
Face Image ◽  
Discriminative Learning ◽  
Low Resolution ◽  
Face Images ◽  
World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

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