Robust Fingerprint Recognition Research Articles

MINUTIA EXTRACTOR AND MINUTIA MATCHER BASED FINGER PRINT RECOGNITION USING DIGITAL IMAGE PROCESSING

The MATLAB code presents a user-friendly graphical interface for fingerprint recognition and verification. Through this interface, users can load fingerprint images and execute a series of image processing tasks aimed at enhancing image quality and extracting crucial features. Operations such as histogram equalization, FFT enhancement, binarization, and thinning are available to optimize the fingerprint images for analysis. The system facilitates minutia extraction, identifying significant points like ridge endings and bifurcations, which are fundamental for fingerprint matching. Moreover, it includes functionalities for removing spurious minutiae, thereby improving the accuracy of the recognition process. Users can save and load fingerprint templates, streamlining the storage and retrieval of extracted features for matching purposes. Additionally, the system supports fingerprint matching, allowing users to compare templates and determine their similarity or match percentage. This versatile tool finds applications in biometric authentication systems, forensic analysis, and security domains, where robust fingerprint recognition and verification are imperative. Overall, the MATLAB code provides a comprehensive framework encapsulated within a user-friendly interface, offering researchers and practitioners a valuable resource for fingerprint analysis and biometric authentication. Keywords : Fingerprint recognition, GUI (Graphical User Interface), Security systems, Access control, Forensic analysis, User-friendly

Robust fingerprint recognition approach based on diagonal slice of polyspectra in the polar space

Although fingerprint recognition is a mature technology and nowadays commercial state-of-the-art systems can be successfully used in a number of real applications, not all the problems have been solved and the research is still very active in the field. This paper presents a new approach for estimating the shift and rotation parameters between fingerprint images stored in the dadabase that operates in the third-order frequency-domain measure called the auto-bispectrum, which, allows us to estimate the shift and the rotation separately. The diagonal slices and their spectra of auto- and cross-bispectrum are proposed. The rotation parameters are estimated from the remaining polar sampled the third-order spectrum using cross-correlation, and then, after compensating for rotation, we may easily estimate the translational component, e.g., by using phase correlation.Experimental evidence of this performance is presented, and the mathematical reasons behind these characteristics are explained in depth. We compare our approach in a simulation to other frequency-domain fingerprint recognition algorithms. We find that our algorithm can better estimate shift and rotation parameters than the other methods.

A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network

Automatic fingerprint recognition has received significant attention because of its excellent fingerprint stability characteristics. Fingerprints will now explode in popularity as online stores allow payments and secure smartphones. The main objective of this study is to develop a fingerprint recognition system to identify individuals by using a new method, termed as ‘Multi-Connection Hopfield Neural Network’. This might pave the way for the development of a more robust fingerprint recognition system that is more accurate and capable of working in noisy environments. Furthermore, such a system makes operations less complex and saves memory by employing additional tranches of auto-associative memory. This study uses three databases, namely FVC-2004, International NIST-4, and internal database, containing 80, 2000, and 2000 fingerprint images. The proposed fingerprint recognition system achieves 99.65% accuracy without noise. This study also shows that the proposed system works well in the presence of noise and achieves 96.07% accuracy in the presence of 50% random noise.

Robust and high-security fingerprint recognition system using optical coherence tomography

Traditional fingerprint recognition systems are vulnerable to attacks, such as the use of artificial fingerprints, and poor performance will be achieved if the captured surface fingerprints are of low-quality. Developing high-security and robust fingerprint recognition systems is of increasing concern in modern society. The introduction of optical coherence tomography (OCT) for fingerprint imaging opens up a new research domain for fingerprint recognition due to its ability to capture the depth information of skin layers. This paper proposes a fingerprint recognition system based on OCT. The research first establishes a database with normal, worn-out, artificial and degraded fingerprints imaged by our custom-built, in-house OCT device. Then, we propose to reconstruct three layers of subsurface fingerprints by considering diverse skin layer information. For each of the subsurface fingerprints divided according to the physical structure of the fingertip, we propose a simple yet effective projection-based reconstruction method. Finally, a pixel-level fusion strategy based on the local image quality is proposed to the fuse the three levels of subsurface fingerprints for robust fingerprint recognition. In our experiments, we show that as much diverse fingerprint information can be retained as possible by the proposed subsurface fingerprint reconstruction method. We also demonstrate the effectiveness and efficiency of the proposed method by comparing it with existing state-of-the-art internal fingerprint reconstruction approaches. The robustness and high anti-spoofing ability of the proposed system is verified by comparing the matching performance evaluated on the established OCT-based database and another database with the same fingerprints imaged by a commercial optical sensor. The best EER and FMR100 evaluated on OCT-based fingerprints are 0.42% and 0.36%, respectively. The best EER and FMR100 evaluated on traditional 2D surface fingerprints are 8.05% and 18.18%, respectively, which shows the vast potential of the proposed system in current automated fingerprint recognition systems (AFRSs).

Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations

We present the design and manufacturing of high-fidelity universal 3D fingerprint targets, which can be imaged on a variety of fingerprint sensing technologies, namely, capacitive, contact optical, and contactless optical. Universal 3D fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of fingerprint readers but also the ability to conduct fingerprint reader interoperability studies. Fingerprint reader interoperability refers to how robust fingerprint recognition systems are to variations in the images acquired by different types of fingerprint readers. To build universal 3D fingerprint targets, we adopt a molding and casting framework consisting of: 1) digital mapping of fingerprint images to a negative mold; 2) CAD modeling a scaffolding system to hold the negative mold; 3) fabricating the mold and scaffolding system with a high resolution 3D printer; 4) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger; and 5) fabricating a 3D fingerprint target using controlled casting. Our experiments conducted with personal identity verification and Appendix F certified optical (contact and contactless) and capacitive fingerprint readers demonstrate the usefulness of universal 3D fingerprint targets for controlled and repeatable fingerprint reader evaluations and also fingerprint reader interoperability studies.

TEXTURE-BASED FINGERPRINT RECOGNITION COMBINING DIRECTIONAL FILTER BANKS AND WAVELET

To design an effective and robust fingerprint recognition method is still an open issue. Some texture-based methods such as directional energy method and wavelet method are available nowadays. However, directional energy method is insufficient to capture the detail information of fingerprint and it is also improper to directly use wavelet method to extract the feature since the complex and rich edge information of fingerprint. In this work we propose a texture-based method called DFB-Wavelet for fingerprint recognition via combining directional filter banks (DFB) and wavelet. The region of interest (ROI) composed of nonoverlapping square blocks, is decomposed into eight directions by employing DFB. Wavelet signatures are calculated as the features of a fingerprint image from each directional subband of DFB. The feature matching is performed on the global normalized Euclidean distance between the input fingerprint features and the templates features. Experimental results show that DFB-Wavelet method has the higher accuracy compared to the traditional texture-based methods.

Multibiometric Identification System Based On Score Level Fusion

Biometric identification systems, which use physical features to check a person’s identity, ensures much greater security than password and number systems. Multibiometric system is being increasingly deployed in much large scale application because they provide lower error rate, large population coverage compared to unibiometric. In this paper, multibiometric identification system aim to fuse iris n fingerprint traits. During enrollment stage system generate iris n fingerprint template separately n stored in database. Approach for fingerprint recognition is to extract minutiae from fingerprint images. It makes possible to achieve highly robust fingerprint recognition for low-quality fingerprints. During iris recognition images are segmented, normalized and features are extract by using Log-Gabor filter. Finally matching is done with help of hamming-distance. Once both iris n fingerprint template are match separately scores are combined by using sum rule-based score level fusion which increase the recognition rate. Thus improve system accuracy and dependability. Keywordsbiometric, minutiae extraction, Log-Gabor filter, sum-rule based score level fusion, identification system

Fingerprint Reference-Point Detection

A robust fingerprint recognition algorithm should tolerate the rotation and translation of the fingerprint image. One popular solution is to consistently detect a unique reference point and compute a unique reference orientation for translational and rotational alignment. This paper develops an effective algorithm to locate a reference point and compute the corresponding reference orientation consistently and accurately for all types of fingerprints. To compute the reliable orientation field, an improved orientation smoothing method is proposed based on adaptive neighborhood. It shows better performance in filtering noise while maintaining the orientation localization than the conventional averaging method. The reference-point localization is based on multiscale analysis of the orientation consistency to search the local minimum. The unique reference orientation is computed based on the analysis of the orientation differences between the radial directions from the reference point, which are the directions of the radii emitted from the reference point with equivalent angle interval, and the local ridge orientations along these radii. Experimental results demonstrate that our proposed algorithm can consistently locate a unique reference point and compute the reference orientation with high accuracy for all types of fingerprints.