Proceedings ArticleDOI
Can holistic representations be used for face biometric quality assessment
Samarth Bharadwaj,Mayank Vatsa,Richa Singh +2 more
- pp 2792-2796
TLDR
This paper investigates the use of holistic super-ordinate representations, namely, Gist and sparsely pooled Histogram of Orientated Gradient, in classifying images into different quality categories that are derived from matching performance.Abstract:
A face quality metric must quantitatively measure the usability of an image as a biometric sample. Though it is well established that quality measures are an integral part of robust face recognition systems, automatic measurement of bio-metric quality in face is still challenging. Inspired by scene recognition research, this paper investigates the use of holistic super-ordinate representations, namely, Gist and sparsely pooled Histogram of Orientated Gradient (HOG), in classifying images into different quality categories that are derived from matching performance. The experiments on the CAS-PEAL and SCFace databases containing covariates such as illumination, expression, pose, low-resolution and occlusion by accessories, suggest that the proposed algorithm can efficiently classify input face image into relevant quality categories and be utilized in face recognition systems.read more
Citations
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Journal ArticleDOI
Biometric quality: a review of fingerprint, iris, and face
TL;DR: The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.
Journal ArticleDOI
Learning Face Image Quality From Human Assessments
Lacey Best-Rowden,Anil K. Jain +1 more
TL;DR: This is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.
Posted Content
Face Image Quality Assessment: A Literature Survey
Torsten Schlett,Christian Rathgeb,Olaf Henniger,Javier Galbally,Julian Fierrez,Christoph Busch +5 more
TL;DR: This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input and a trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches.
Journal ArticleDOI
Face biometric quality assessment via light CNN
TL;DR: This paper considered five categories of common homogeneous distortion in video suvillance applications, i.e. low-resolution, blurring, additive Gaussian white noise, salt and pepper noise, and Poisson noise and proposed a novel biometric quality assessment (BQA) method for face images and explored its applications in face recognition.
Journal Article
Standardization of Face Image Sample Quality
TL;DR: This paper proposes an approach for standardization of facial image quality, and develops facial symmetry based methods for the assessment of it by measuring facial asymmetries caused by non-frontal lighting and improper facial pose.
References
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Book ChapterDOI
Face Quality Assessment System in Video Sequences
TL;DR: A system based on four simple features including out-of-plan rotation, sharpness, brightness and resolution, to assess the face quality in a video sequence using both a local scoring system and weights is presented.
Proceedings ArticleDOI
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TL;DR: This framework employs a novel classification-based score normalization process for various quality metrics and includes techniques to fuse those individual quality scores into an overall quality score which is shown to be correlated to the genuine match scores of the Facelt face recognition engine.
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Proceedings ArticleDOI
Face image validation system
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