Block-Based Bag of Words for Robust Face Recognition under Variant Conditions of Facial Expression, Illumination, and Partial Occlusion

2011 ◽  
Vol E94-A (2) ◽  
pp. 533-541 ◽  
Author(s):  
Zisheng LI ◽  
Jun-ichi IMAI ◽  
Masahide KANEKO
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Bag Of Words ◽  
Partial Occlusion ◽  
Robust Face Recognition ◽  
Block Based

scholarly journals Efficient Detection of Occlusion prior to Robust Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Rui Min ◽  
Abdenour Hadid ◽  
Jean-Luc Dugelay
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Video Surveillance ◽  
Real World ◽  
State Of The Art ◽  
Efficient Detection ◽  
Art Works ◽  
Enormous Amount ◽  
Recognition Systems ◽  
Robust Face Recognition

While there has been an enormous amount of research on face recognition under pose/illumination/expression changes and image degradations, problems caused by occlusions attracted relatively less attention. Facial occlusions, due, for example, to sunglasses, hat/cap, scarf, and beard, can significantly deteriorate performances of face recognition systems in uncontrolled environments such as video surveillance. The goal of this paper is to explore face recognition in the presence of partial occlusions, with emphasis on real-world scenarios (e.g., sunglasses and scarf). In this paper, we propose an efficient approach which consists of first analysing the presence of potential occlusion on a face and then conducting face recognition on the nonoccluded facial regions based on selective local Gabor binary patterns. Experiments demonstrate that the proposed method outperforms the state-of-the-art works including KLD-LGBPHS, S-LNMF, OA-LBP, and RSC. Furthermore, performances of the proposed approach are evaluated under illumination and extreme facial expression changes provide also significant results.

scholarly journals Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

2021 ◽  
Vol 15 (3) ◽  
pp. 47-57
Author(s):  
Amit Kumar Yadav ◽  
Neeraj Gupta ◽  
Aamir Khan ◽  
Anand Singh Jalal
Keyword(s):  
Face Recognition ◽  
Kernel Method ◽  
Partial Occlusion ◽  
Scale Invariant ◽  
Query Image ◽  
Art Methods ◽  
Constrained Environments ◽  
The Face ◽  
Robust Face Recognition ◽  
Scale Invariant Feature

Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task in the field of computer vision. Although the various state-of-the-art methods of face recognition in constrained environments have achieved satisfactory results, there are still many issues which are untouched in unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, the authors have proposed an approach which utilized the local generic feature (LGF) to recognize the face in the partial occlusion by fusing features scale invariant feature transform (SIFT) and multi-block local binary pattern (MB-LBP). It also utilizes robust kernel method for classification of the query image. They have validated the effectiveness of the proposed approach on the benchmark AR face database. The experimental outcomes illustrate that the proposed approach outperformed the state-of-art methods for robust face recognition.

Bisecting k-means clustering based face recognition using block-based bag of words model

Optik ◽  
2015 ◽  
Vol 126 (19) ◽  
pp. 1761-1766 ◽  
Author(s):  
Chunhui Zhao ◽  
Xiaocui Li ◽  
Yan Cang
Keyword(s):  
Face Recognition ◽  
Bag Of Words ◽  
Block Based

scholarly journals A Fast Iterative Pursuit Algorithm in Robust Face Recognition Based on Sparse Representation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhao Jian ◽  
Huang Luxi ◽  
Jia Jian ◽  
Xie Yu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Recognition Performance ◽  
Stopping Rules ◽  
Partial Occlusion ◽  
Slow Response ◽  
Robust Version ◽  
Robust Face Recognition

A relatively fast pursuit algorithm in face recognition is proposed, compared to existing pursuit algorithms. More stopping rules have been put forward to solve the problem of slow response of OMP, which can fully develop the superiority of pursuit algorithm—avoiding to process useless information in the training dictionary. For the test samples that are affected by partial occlusion, corruption, and facial disguise, recognition rates of most algorithms fall rapidly. The robust version of this algorithm can identify these samples automatically and process them accordingly. The recognition rates on ORL database, Yale database, and FERET database are 95.5%, 93.87%, and 92.29%, respectively. The recognition performance under various levels of occlusion and corruption is also experimentally proved to be significantly enhanced.

Sign in / Sign up

Export Citation Format

Share Document