A hybrid fingerprint matcher

By Arun A. Ross, Anil K. Jain, James G. Reisman. Object recognition supported by user interaction for service robots, 2002

452 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e07849fed7a3bd03cc916f2dc211f267cfc1b39a

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In Computer Vision and Pattern Recognition

In Information Systems

In Signal Processing

Citations in the corpus

A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation (2019, background): “Some authors [2], [27], [52], [54], [55], [60], [64], [65]”
Fingerprint recognition by combining global structure and local cues (2006, methodology): “It shows that our system is more robust and accurate than conventional minutiae-based methods, and it also outperforms the previous methods proposed in [6], [14].”
Multimodal biometrics: An overview (2004, background): “[11] combine the matching score of a minutiae-based fingerprint matcher with that of a texture-based matcher to improve matching performance.”
Combining minutiae descriptors for fingerprint matching (2008, methodology): “In our opinion, ridge-based descriptors are less discriminating than the other types, mainly because only one associated ridge is used in Refs.”
An Off-line Signature Verification System Based on Fusion of Local and Global Information (2004, background): “…combined utilization of global texture features and local minutiae-based descriptors has been shown to improve veriﬁcation performance [10], or speaker veriﬁcation, where the combination of prosodic descriptors and local acoustic features out-performs the state-of-the-art acoustic approach [11].”
Biometric Template Security (2008, methodology): “Figure 11: ROC curves of random multispace quantization (RMQ) [47] using the texture features proposed in [88].”
Efficient Privacy-Preserving Biometric Identification (2011, background): “In the last decade, many fingerprint-recognition techniques have been developed that combine various features of the fingerprint [2, 18, 19, 21].”
Gabor Filter-Based Fingerprint Anti-spoofing (2008, background): “It is well known that, Gabor filters can optimally capture local frequency and orientation information [2], [ 3 ]; so we use Gabor features to characterize textural differences in real and spoof fingerprints.”