NDLI: Augmented SRC for face recognition under quality distortions

Content Provider	IET Digital Library
Author	Mounsef, Jinane Karam, Lina
Abstract	In the last two decades, numerous methods have been developed to offer a formulation to the face recognition problem under scene-dependent conditions. However, these methods have not considered image quality degradations resulting from capture, processing, and transmission such as blur and occlusion due to packet loss, under the same scene variations. Although deep neural networks are achieving state-of-the-art results on face recognition, the existing networks are susceptible to quality distortions. In this work, the authors propose an augmented sparse representation classifier (SRC) framework to improve the performance of the conventional SRC in the presence of Gaussian blur, camera shake blur, and block occlusions, while preserving its robustness to scene-dependent variations. In their evaluation of the SRC framework, they present a feature sparsity concentration and classification index that is capable of assessing the quality of features in terms of recognition accuracy as well as class-based sparsity concentration. For this purpose, they consider three main types of features including image raw pixels, histogram of oriented gradients and deep learning visual geometry group (VGG) Face. The obtained performance results show that the proposed method outperforms state-of-the-art sparse-based and blur-invariant methods.
Starting Page	431
Ending Page	442
Page Count	12
ISSN	20474938
Volume Number	8
e-ISSN	20474946
Issue Number	Issue 6, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/8/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5242
Journal	IET Biometrics
Publisher Date	2019-08-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Augmented Sparse Representation Classifier Framework Augmented SRC Block Occlusions Camera Shake Blur Class-based Sparsity Concentration Classification Index Computer Vision And Image Processing Technique Deep Learning VGG-Face Deep Neural Network Face Recognition Face Recognition Problem Feature Extraction Feature Sparsity Concentration Gaussian Blur Gaussian Processes Histogram of Oriented Gradients Image Classification Image Quality Degradations Image Raw Pixel Image Recognition Image Representation Image Restoration Learning in AI Neural Computing Technique Neural Nets Packet Loss Performance Improvement Quality Distortion Recognition Accuracy Scene Variations Scene-dependent Condition Scene-dependent Variations Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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