Changlong Jin

Liveness detection of fingerprint based on band-selective Fourier spectrum

This paper proposes a novel method for fingerprint liveness detection based on band-selective Fourier spectrum. The 2D spectrum of a fingerprint image reflects the distribution and strength in spatial frequencies of ridge lines. The ridge-valley texture of the fingerprint produces a ring pattern around the center in the Fourier spectral image and a harmonic ring pattern in the subsequent ring. Both live and fake fingerprints produce these rings, but with different amplitudes in different spatial frequency bands. Typically, live fingerprints show stronger Fourier spectrum in the ring patterns than the fake. The proposed method classifies the live and the fake fingerprints by analyzing the band-selective Fourier spectral energies in the two ring patterns. The experimental results demonstrate this approach to be a promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing vulnerabilities.

Pixel-level singular point detection from multi-scale Gaussian filtered orientation field

Singular point, as a global feature, plays an important role in fingerprint recognition. Inconsistent detection of singular points apparently gives an affect to fingerprint alignment, classification, and verification accuracy. This paper proposes a novel approach to pixel-level singular point detection from the orientation field obtained by multi-scale Gaussian filters. Initially, a robust pixel-level orientation field is estimated by a multi-scale averaging framework. Then, candidate singular points in pixel-level are extracted from the complex angular gradient plane derived directly from the pixel-level orientation field. The candidate singular points are finally validated via a cascade framework comprised of nested Poincare indices and local feature-based classification. Experimental results over the FVC 2000 DB2 confirm that the proposed method achieves robust and accurate orientation field estimation and consistent pixel-level singular point detection. The experimental results exhibit a low computational cost with better performance. Thus, the proposed method can be employed in real-time fingerprint recognition.

Comparative Assessment of Fingerprint Sample Quality Measures Based on Minutiae-Based Matching Performance

This Fingerprint sample quality is one of major factors influencing the matching performance of fingerprint recognition systems. The error rates of fingerprint recognition systems can be decreased significantly by removing poor quality fingerprints. The purpose of this paper is to assess the effectiveness of individual sample quality measures on the performance of minutiae-based fingerprint recognition algorithms. Initially, the authors examined the various factors that influenced the matching performance of the minutiae-based fingerprint recognition algorithms. Then, the existing measures for fingerprint sample quality were studied and the more effective quality measures were selected and compared with two image quality software packages, (NFIQ from NIST, and QualityCheck from Aware Inc.) in terms of matching performance of a commercial fingerprint matcher (Verifinger 5.0 from Neurotechnologija). The experimental results over various Fingerprint Verification Competition (FVC) datasets show that even a single sample quality measure can enhance the matching performance effectively.

Fingerprint liveness detection based on multiple image quality features

Recent studies have shown that the conventional fingerprint recognition systems are vulnerable to fake attacks, and there are many existing systems that need to update their anti-spoofing capability inexpensively. This paper proposes an image quality-based fake detection method to address this problem. Three effective fake/live quality measures, spectral band energy, middle ridge line and middle valley line, are extracted firstly, and then, these features are fused and tested on a fake/live dataset using SVM and QDA classifiers. Experimental results demonstrate that the proposed method is promising in increasing the security of the existing fingerprint authentication system by only updating the software.

High-resolution orientation field estimation based on multi-scale Gaussian filter

Orientation field plays the most important role in fingerprint recognition. Proposed in this paper is a novel approach of pixel-wise orientation field estimation using multi-scale Gaussian filter. A three-stage averaging framework in pixel-scale, block-scale, and orientation-scale is developed for handling gradient vectors, coherence data, and orientation vectors, respectively. Experimental results on various FVC datasets show the proposed algorithm achieves accurate orientation field estimation which is robust to local defects, such as scar, low contrast, ridge discontinuity, smudged area, etc. with a low computational cost.

Assessing the Difficulty Level of Fingerprint Datasets Based on Relative Quality Measures

Understanding the difficulty of a dataset is of primary importance when it comes to testing and evaluating fingerprint recognition systems or algorithms because the evaluation result is dependent on the dataset. Proposed in this paper is a general framework of assessing the level of difficulty of fingerprint datasets based on quantitative measurements of not only the sample quality of individual fingerprints but also relative differences between genuine pairs, such as common area and deformation. The experimental results over multi-year FVC datasets demonstrate that the proposed method can predict the relative difficulty levels of the fingerprint datasets which coincide with the equal error rates produced by two matching algorithms. The proposed framework is independent of matching algorithms and can be performed automatically.

Image Quality and Minutiae Count Comparison for Genuine and Artificial Fingerprints

The vulnerabilities of biometric sensors have been discussed extensively in the literature and popularized in films and television shows. This research examines the image quality of an artificial print as compared to a genuine finger, and examines the characteristics of the two, including minutiae counts and image quality, as repeated samples are taken.

Type-Independent Pixel-Level Alignment Point Detection for Fingerprints

Robust alignment point detection is still a challenging problem in fingerprint recognition, especially for arch type fingerprints. Proposed in this paper is a method of detecting a pixel-level alignment point from mated fingerprints regardless of the type based on pixel-level orientation field. Given a fingerprint, firstly, pixel-level orientation field is computed using multi-scale Gaussian filtering. Secondly, a vertical symmetry line is extracted from the orientation field, based on which the fingerprint type is classified, either arch or non-arch type. For non-arch mated pairs, the pixel-level singular points (core or delta) are adopted as candidate alignment points and be verified by point-pattern matching and the average orientation difference between the orientation fields. And, for arch mated pairs, the alignment points are detected at the maximum in the angular difference and the orientation certainty level over the symmetry lines. The proposed method is tested over the FVC 2000 DB2a, and 95.93% mated fingerprint pairs are aligned within one ridge-width displacement.

Method for pedestrian detection using ground plane constraint based on vision sensor

Challenging problems pertaining to pedestrian detection still exist in Advanced Drive Assistance Systems. This paper presents a novel method to reduce erroneous pedestrian detection by moving vehicles. The algorithm utilizes camera parameters to calculate the distances between the moving vehicle and objects, and then builds ground plane constraints to detect real pedestrians. The proposed method not only reduces the number of false positives but also determines the distance of objects relative to the moving vehicle.