A neural-based minutiae pair identification method for touch-less fingerprint images

Contact-based sensors are the traditional devices used to capture fingerprint images in commercial and homeland security applications. Contact-less systems achieve the fingerprint capture by vision systems avoiding that users touch any parts of the biometric device. Typically, the finger is placed in the working area of an optics system coupled with a CCD module. The captured light pattern on the finger is related to the real ridges and valleys of the user fingertip, but the obtained images present important differences from the traditional fingerprint images. These differences are related to multiple factors such as light, focus, blur, and the color of the skin. Unfortunately, the identity comparison methods designed for fingerprint images captured with touch-based sensors do not obtain sufficient accuracy when are directly applied to touch-less images. Recent works show that multiple views analysis and 3D reconstruction can enhance the final biometric accuracy of such systems. In this paper we propose a new method for the identification of the minutiae pairs between two views of the same finger, an important step in the 3D reconstruction of the fingerprint template. The method is divisible in the sequent tasks: first, an image preprocessing step is performed; second, a set of candidate minutiae pairs is selected in the two images, then a list of candidate pairs is created; last, a set of local features centered around the two minutiae is produced and processed by a classifier based on a trained neural network. The output of the system is the list of the minutiae pairs present in the input images. Experiments show that the method is feasible and accurate in different light conditions and setup configurations

Global and local feature-based transformations for fingerprint data protection

Due to its non-shareable characteristic, biometrics has been widely implemented for authenticating users. This characteristic asserts that biometrics meets the non-repudiation requirement which is one of the key factors in the authentication system. Among biometric modalities, fingerprints have the best capability for satisfying both technical and social aspects of an authentication system. Nevertheless, similar to other modalities, once the stored fingerprint template has been compromised, the effect will be forever since the fingerprint pattern is permanent. So, a mechanism which can protect this fingerprint pattern is desired. Common cryptographic approaches, however, do not work due to uncertainty in the captured fingerprint image caused by disturbing factors either in the scanner or in the finger itself. While authenticating fingerprints in a plain format is not secure, in a cipher format it is impractical because slightly different inputs result in completely different outputs. Therefore, a specific transformation mechanism is needed: one which is able to accept similar fingerprints and reject dissimilar fingerprints, while at the same time generating a relatively non-invertible fingerprint template. Most of the existing protection approaches, however, have high error rates which make them inappropriate to implement. The approaches proposed in this thesis are for addressing this problem, in particular. The proposed approaches comprise three modules: feature transformation, feature representation and feature comparison. The evaluation is to measure the accuracy, the capability for revoking the template and generating another template, and the capability for scrambling the fingerprint pattern. The first approach, which is a global feature-based transformation, is developed by exploring both the fingerprint singular point and minutiae points. The experimental results show that this approach is able to improve the existing performance, despite possible limitations (e.g., relying on the core point). In order to eliminate possible drawbacks of that global feature-based transformation, a local-based transformation is implemented by extracting only minutiae points. This has been able to eliminate the core-point dependency and at the same time produce only a slightly higher error rate than the previous proposed approach. To make further improvements, the third approach is designed in both Cartesian and polar coordinate spaces. This approach has been able to take advantages of being core point independent and at the same time generates higher performance than most of the existing approaches

Automatic Fingerprint Verification Using Neural Networks

Abstract. This paper presents an application of Learning Vector Quantization (LVQ) neural network (NN) to Automatic Fingerprint Verification (AFV). The new approach is based on both local (minutiae) and global image features (shape signatures). The matched minutiae are used as reference axis for generating shape signatures which are then digitized to form a feature vector describing the fingerprint. A LVQ NN is trained to match the fingerprints using the difference of a pair of feature vectors. The results show that the integrated system significantly outperforms the minutiae-based system alone in terms of classification accuracy. It also confirms the ability of the trained NN to have consistent performance on unseen databases.