Open AccessJournal Article
Standardization of Face Image Sample Quality
TLDR
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.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. 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. Experimental results are provided to illustrate the concepts, definitions and effectiveness.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
Design and evaluation of photometric image quality measures for effective face recognition
TL;DR: A new face image quality index (FQI) is proposed that combines multiple quality measures, and classifies a face image based on this index, and conducts statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.
Proceedings ArticleDOI
Assessing face image quality for smartphone based face recognition system
TL;DR: This work constructed a new database of 101 individuals with 22 frontal face images with different facial pose angles, illumination and at five different distances between the subject and the mobile device and proposes a new quality metric based on vertical edge density that can robustly estimate the pose variations and improves the quality estimation of a face image.
Proceedings ArticleDOI
Face image quality assessment for face selection in surveillance video using convolutional neural networks
TL;DR: This work proposes a FQA algorithm based on mimicking the recognition capability of a given FR algorithm using a Convolutional Neural Network (CNN), which can be used in conjunction with any FR algorithm.
Proceedings ArticleDOI
Automatic Face Quality Assessment from Video Using Gray Level Co-occurrence Matrix: An Empirical Study on Automatic Border Control System
TL;DR: The experimental results have indicated that the proposed face quality assessment algorithm can effectively classify the input image into relevant quality bins that in turn can be employed for the improved face verification.
References
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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
Design and evaluation of photometric image quality measures for effective face recognition
TL;DR: A new face image quality index (FQI) is proposed that combines multiple quality measures, and classifies a face image based on this index, and conducts statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.
Proceedings ArticleDOI
Assessing face image quality for smartphone based face recognition system
TL;DR: This work constructed a new database of 101 individuals with 22 frontal face images with different facial pose angles, illumination and at five different distances between the subject and the mobile device and proposes a new quality metric based on vertical edge density that can robustly estimate the pose variations and improves the quality estimation of a face image.
Proceedings ArticleDOI
Face image quality assessment for face selection in surveillance video using convolutional neural networks
TL;DR: This work proposes a FQA algorithm based on mimicking the recognition capability of a given FR algorithm using a Convolutional Neural Network (CNN), which can be used in conjunction with any FR algorithm.
Book ChapterDOI
Evaluation of Biometric Systems
TL;DR: The objective of this chapter is to answer questions about biometrics, by presenting an evaluation methodology of biometric systems, and to put into obviousness the benefit of a new biometric system.