Book ChapterDOI
An efficient finger-knuckle-print based recognition system fusing SIFT and SURF matching scores
G. S. Badrinath,Aditya Nigam,Phalguni Gupta +2 more
- pp 374-387
TLDR
A novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation and evaluated against various scales and rotations of the query image.Abstract:
This paper presents a novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation. The non-uniform brightness of the FKP due to relatively curvature surface is corrected and texture is enhanced. The local features of the enhanced FKP are extracted using the scale invariant feature transform (SIFT) and the speeded up robust features (SURF). Corresponding features of the enrolled and the query FKPs are matched using nearest-neighbour-ratio method and then the derived SIFT and SURF matching scores are fused using weighted sum rule. The proposed system is evaluated using PolyU FKP database of 7920 images for both identification mode and verification mode. It is observed that the system performs with CRR of 100% and EER of 0.215%. Further, it is evaluated against various scales and rotations of the query image and is found to be robust for query images downscaled upto 60% and for any orientation of query image.read more
Citations
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Journal ArticleDOI
Knuckle Print Biometrics and Fusion Schemes -- Overview, Challenges, and Solutions
TL;DR: The survey in this article focuses on the interface between various hand modalities, summary of inner- and dorsal-knuckle print recognition, and fusion techniques and conclusions related to the scope of knuckle print as a biometric trait are drawn.
Journal ArticleDOI
Defocus Blur-Invariant Scale-Space Feature Extractions
Elhusain Saad,Keigo Hirakawa +1 more
TL;DR: Modifications to scale-space feature extraction techniques scale-invariant feature transform (SIFT) and speeded up robust features (SURFs) that make the feature detection and description invariant to defocus blur are proposed.
Proceedings ArticleDOI
Quality assessment of knuckleprint biometric images
Aditya Nigam,Phalguni Gupta +1 more
TL;DR: This is the first attempt to automatically assess the quality of knuckleprint images, and an effort has been made to identify, estimate and quantify some of these quality attributes and fuse them to obtain an overall quality score for anyknuckleprint image.
Proceedings ArticleDOI
Multi-scale shift local binary pattern based-descriptor for finger-knuckle-print recognition
TL;DR: This paper proposes using the Multi-scale Shift Binary Pattern (MSLBP) descriptor which extends the original SLBP to multi-scale to get more robust and discriminative representation of FKP features.
Book ChapterDOI
A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN.
Yikui Zhai,Cao He,Lu Cao,Hui Ma,Gan Junying,Junying Zeng,Vincenzo Piuri,Fabio Scotti,Wenbo Deng,Yihang Zhi,Wang Jinxin +10 more
TL;DR: Compared with traditional feature extraction method, the proposed batch-normalized Convolutional Neural Network architecture with data augmentation for FKP recognition can not only extract more discriminative features, but also improve the accuracy of FkP recognition.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
BookDOI
Handbook of Biometrics
TL;DR: This book addresses the void in biometrics research by inviting some of the prominent researchers in Biometrics to contribute chapters describing the fundamentals as well as the latest innovations in their respective areas of expertise.