Standardization of face image sample quality
Xiufeng Gao,Stan Z. Li,Rong Liu,Peiren Zhang +3 more
- pp 242-251
TLDR
An approach for standardization of facial image quality is presented, and facial symmetry based methods for its assessment by which facial asymmetries caused by non-frontal lighting and improper facial pose can be measured are developed.Abstract:
Performance of biometric systems is dependent on quality of acquired biometric samples. Poor sample quality is a main reason for matching errors in biometric systems and may be the main weakness of some implementations. In this paper, we present an approach for standardization of facial image quality, and develop facial symmetry based methods for its assessment by which facial asymmetries caused by non-frontal lighting and improper facial pose can be measured. Experimental results are provided to illustrate the concepts, definitions and effectiveness.read more
Citations
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Journal ArticleDOI
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
TL;DR: As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial imageAnalysis, are also highlighted.
Proceedings ArticleDOI
Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition
TL;DR: An efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face is proposed.
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
A Unified Framework for Biometric Expert Fusion Incorporating Quality Measures
Norman Poh,J. Kittler +1 more
TL;DR: A general Bayesian framework that can utilize the quality information effectively is proposed that encompasses several recently proposed quality-based fusion algorithms in the literature and improves the understanding of the role of quality in multiple classifier combination.
Journal ArticleDOI
Face Image Quality Assessment Based on Learning to Rank
TL;DR: A learning to rank based framework for assessing the face image quality is proposed and Experimental result demonstrates its effectiveness in improving the robustness of face detection and recognition.
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