Face Recognition With Partial Occlusion Based on Attention Mechanism

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.

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Fourier Spectrum Features for Face Recognition

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7469.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2632-2636

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Fourier Spectrum ◽

Partial Occlusion ◽

Novel Approach ◽

The Face ◽

Unsupervised Methods

The objective is to introduce a novel approach which deals with the challenges: uneven illumination and partial occlusion. This method performs face recognition by extracting the magnitude spectra features. At each point on the face, largest matching areas were found. Thus robustness is achieved using Fourier magnitude spectra feature extraction and largest matching area comparison. This method performs competitively with corrupted images and other unsupervised methods. The proposed approach is experimented on Yale B and AR datasets.

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Unconstrained Face Recognition

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch068 ◽

2018 ◽

pp. 1640-1661

Author(s):

Stefanos Zafeiriou ◽

Irene Kotsia ◽

Maja Pantic

Keyword(s):

Computer Vision ◽

Face Recognition ◽

Partial Occlusion ◽

Human Face ◽

Facial Analysis ◽

Identity Recognition ◽

Unconstrained Face Recognition ◽

Fully Automatic ◽

Unconstrained Face ◽

Appearance Changes

The human face is the most well-researched object in computer vision, mainly because (1) it is a highly deformable object whose appearance changes dramatically under different poses, expressions, and, illuminations, etc., (2) the applications of face recognition are numerous and span several fields, (3) it is widely known that humans possess the ability to perform, extremely efficiently and accurately, facial analysis, especially identity recognition. Although a lot of research has been conducted in the past years, the problem of face recognition using images captured in uncontrolled environments including several illumination and/or pose variations still remains open. This is also attributed to the existence of outliers (such as partial occlusion, cosmetics, eyeglasses, etc.) or changes due to age. In this chapter, the authors provide an overview of the existing fully automatic face recognition technologies for uncontrolled scenarios. They present the existing databases and summarize the challenges that arise in such scenarios and conclude by presenting the opportunities that exist in the field.

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Unconstrained Face Recognition

Face Recognition in Adverse Conditions - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-5966-7.ch002 ◽

2014 ◽

pp. 16-37 ◽

Cited By ~ 1

Author(s):

Stefanos Zafeiriou ◽

Irene Kotsia ◽

Maja Pantic

Keyword(s):

Computer Vision ◽

Face Recognition ◽

Partial Occlusion ◽

Human Face ◽

Facial Analysis ◽

Identity Recognition ◽

Unconstrained Face Recognition ◽

Fully Automatic ◽

Unconstrained Face ◽

Appearance Changes

The human face is the most well-researched object in computer vision, mainly because (1) it is a highly deformable object whose appearance changes dramatically under different poses, expressions, and, illuminations, etc., (2) the applications of face recognition are numerous and span several fields, (3) it is widely known that humans possess the ability to perform, extremely efficiently and accurately, facial analysis, especially identity recognition. Although a lot of research has been conducted in the past years, the problem of face recognition using images captured in uncontrolled environments including several illumination and/or pose variations still remains open. This is also attributed to the existence of outliers (such as partial occlusion, cosmetics, eyeglasses, etc.) or changes due to age. In this chapter, the authors provide an overview of the existing fully automatic face recognition technologies for uncontrolled scenarios. They present the existing databases and summarize the challenges that arise in such scenarios and conclude by presenting the opportunities that exist in the field.

Download Full-text