A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple matchers for a high security fingerprint verification system
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Warping Using Ridge Curve Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pores and Ridges: Fingerprint Matching Using Level 3 Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
The surround Imager™: a multi-camera touchless device to acquire 3d rolled-equivalent fingerprints
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Structural hidden Markov models for biometrics: Fusion of face and fingerprint
Pattern Recognition
Level 2 features and wavelet analysis based hybrid fingerprint matcher
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory
International Journal of Approximate Reasoning
Novel Approaches for Exclusive and Continuous Fingerprint Classification
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Wavelet-based multiresolution analysis of ridges for fingerprint liveness detection
International Journal of Information and Computer Security
Fake Fingers in Fingerprint Recognition: Glycerin Supersedes Gelatin
Formal to Practical Security
Beyond Minutiae: A Fingerprint Individuality Model with Pattern, Ridge and Pore Features
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Direct Pore Matching for Fingerprint Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Modelling fingerprint ridge orientation using Legendre polynomials
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Personal authentication using hand vein triangulation and knuckle shape
IEEE Transactions on Image Processing
High resolution partial fingerprint alignment using pore-valley descriptors
Pattern Recognition
Fingerprint skeleton matching based on local descriptor
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Mosaicing touchless and mirror-reflected fingerprint images
IEEE Transactions on Information Forensics and Security
Adaptive fingerprint pore modeling and extraction
Pattern Recognition
Combination of Gabor wavelets and circular Gabor filter for finger-vein extraction
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Comparative study of features for fingerprint indexing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Data acquisition and processing of 3-D fingerprints
IEEE Transactions on Information Forensics and Security
A novel hierarchical fingerprint matching approach
Pattern Recognition
A new fingerprint matching approach using level 2 and level 3 features
Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering
Orientation and anisotropy of multi-component shapes from boundary information
Pattern Recognition
Personal identification based on finger-vein features
Computers in Human Behavior
Automated human identification using ear imaging
Pattern Recognition
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Some issues of biometrics: technology intelligence, progress and challenges
International Journal of Information Technology and Management
Clustering-Based descriptors for fingerprint indexing and fast retrieval
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Finger-Vein recognition based on a bank of gabor filters
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Proceedings of the on Multimedia and security
First investigation of latent fingerprints long-term aging using chromatic white light sensors
Proceedings of the first ACM workshop on Information hiding and multimedia security
Biometric encryption using enhanced finger print image and elliptic curve
International Journal of Electronic Security and Digital Forensics
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Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, Level 1 (pattern), Level 2 (minutia points), and Level 3 (pores and ridge contours). Although latent print examiners frequently take advantage of Level 3 features to assist in identification, Automated Fingerprint Identification Systems (AFIS) currently rely only on Level 1 and Level 2 features. In fact, the Federal Bureau of Investigation's (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi), which is inadequate for capturing Level 3 features, such as pores. With the advances in fingerprint sensing technology, many sensors are now equipped with dual resolution (500 ppi/1,000 ppi) scanning capability. However, increasing the scan resolution alone does not necessarily provide any performance improvement in fingerprint matching, unless an extended feature set is utilized. As a result, a systematic study to determine how much performance gain one can achieve by introducing Level 3 features in AFIS is highly desired. We propose a hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans. Level 3 features, including pores and ridge contours, are automatically extracted using Gabor filters and wavelet transform and are locally matched using the Iterative Closest Point (ICP) algorithm. Our experiments show that Level 3 features carry significant discriminatory information. There is a relative reduction of 20 percent in the equal error rate (EER) of the matching system when Level 3 features are employed in combination with Level 1 and 2 features. This significant performance gain is consistently observed across various quality fingerprint images.