3D Fingerprint Recognition Research Articles

A DETAILED REVIEW ON FINGERPRINT DOOR LOCK SYSTEM

Passwords and Tokens are extremely vulnerable and are easily stolen or lost. A poor password is one of the most common causes of security and data breaches. Hacker attacks target even the strongest passwords. Resetting the password takes a long time and can cause the employee to lose productivity. Biometrics can be used to solve the problem. It is the method of recognizing or confirming individuals based on physiological or behavioral features such as the iris, fingerprints, facial pattern, DNA, speech patterns, and so on. The concept of distinguishing individuals based on their fingerprints goes back thousands of years. It first became famous in the 1970’s. The detection and authentication of fingerprints is the method of fingerprint identification. Fingerprint identification is the most commonly used biometric. This research paper explains the main characteristics of fingerprints and how the Automatic Minutiae Detection process works, as well as comparing 2D and 3D fingerprint recognition

3D Fingerprint Recognition based on Ridge-Valley-Guided 3D Reconstruction and 3D Topology Polymer Feature Extraction.

An automated fingerprint recognition system (AFRS) for 3D fingerprints is essential and highly promising for biometric security. Despite the progress in developing 3D AFRSs, achieving high-quality real-time reconstruction and high-accuracy recognition of 3D fingerprints remain two challenging issues. To address them, we propose a robust 3D AFRS based on ridge-valley (RV)-guided 3D fingerprint reconstruction and 3D topology polymer (TTP) feature extraction. The former considers the unique fingerprint characteristics of the RV and achieves real-time reconstruction. Unlike traditional triangulation-based methods that establish correspondences between points by cross-correlation-based searching, we propose to establish RV correspondences (RVCs) between ridges/valleys by defining and calculating a RVC matrix based on the topology of RV curves. To enhance depth reconstruction, curve-based smoothing is proposed to refine our novel RV disparity map. The TTP feature codes the 3D topology by projecting the 3D minutiae onto multiple planes and extracting their corresponding 2D topologies and has proven to be effective and efficient for 3D fingerprint recognition. Comprehensive experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both reconstruction and recognition accuracy. Also, due to its very short running time, it is appropriate for practical applications.

3D-FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing

“The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface”. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural non-deformed ridge pattern itself. There are already a small number of previous studies that have already started scratching the surface of 3D fingerprint recognition and that should not go overlooked. However, the vast majority of these few successful approaches published so far, are based on the reconstruction of fingerprints from multiple 2D images acquired with different lighting conditions (photometric stereo 3D reconstruction) or acquired from different angles (stereo vision 3D reconstruction). Such reconstruction methods lead in general to 2D fingerprints wrapped over the overall volume of the finger. These volumetric fingerprints have shown some promising performance, but still miss the real depth information of the ridge pattern, which, in the best case scenario, is coarsely estimated during the error-prone reconstruction process. In the present work we take one step further, directly acquiring for the first time in a consistent and repeatable manner, full-3D fingerprint models stored as point-clouds, where each point is defined by its $[x,y,z]$ coordinates. This way, the 3D data is directly measured by the sensor, with no post-processing reconstruction stage required. The complete recognition system developed represents as well an alternative to traditional technology based on minutiae detection. It shows that image-based processing algorithms and descriptors can be successfully applied to the new full-3D data, reaching very competitive results and confirming the high distinctiveness of the models.

Contactless and partial 3D fingerprint recognition using multi-view deep representation

Contactless 3D fingerprint identification has gained significant attentions in recent years as it can offer more hygienic, accurate and ubiquitous personal identification. Despite such advantages, contactless 3D imaging often results in partial 3D fingerprints as it requires relatively higher cooperation from users during the contactless 3D imaging. Such contactless 3D fingerprint images significantly degrade matching accuracy due to partial 3D fingerprint imaging. This paper proposes an end-to-end contactless 3D fingerprint representation learning model based on convolutional neural network (CNN). The proposed model includes one fully convolutional network for fingerprint segmentation and three Siamese networks to learn multi-view 3D fingerprint feature representation. Contactless partial 3D fingerprint identification is a more challenging problem due to its high degree of freedom during contactless 3D fingerprint acquisition and is also addressed by using proposed model. We therefore investigate multi-view 3D fingerprint recognition and partial 3D fingerprint using proposed approach. Comparative experimental results, presented in this paper using state-of-the-art 3D fingerprint recognition method, demonstrate the effectiveness of the proposed multi-view approach and illustrate a significant improvement of state-of-the-art 3D fingerprint recognition methods.

Tetrahedron Based Fast 3D Fingerprint Identification Using Colored LEDs Illumination

Emerging 3D fingerprint recognition technologies have attracted growing attention in addressing the limitations from contact-based fingerprint acquisition and improve recognition accuracy. However, the complex 3D imaging setups employed in these systems typically require structured lighting with scanners or multiple cameras which are bulky with higher cost. This paper presents a more accurate and efficient 3D fingerprint identification approach using a single 2D camera with multiple colored LED illumination. A 3D minutiae tetrahedron based algorithm is developed to more efficiently match recovered minutiae features in 3D space and address the limitations of 3D minutiae matching approach in the literature. This algorithm significantly improves the matching time to about 15 times than the state-of-art in the reference. A hierarchical tetrahedron matching scheme is also developed to further improve the matching accuracy with faster speed. The 2D images acquired to reconstruct the 3D fingerprints are also used to recover 2D minutiae and further improve matching performance for 3D fingerprints. A new two-session database acquiring from 300 different clients consists of 2760 3D fingerprints reconstructed from 5520 colored 2D fingerprints is also developed and shared in public domain to further advance much needed research in this area. Extensive experimental results presented in this paper validate our approach and demonstrate the effectiveness of proposed algorithms.

Case study of 3D fingerprints applications.

Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this paper firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted. The applications of these features are finally discussed. A series of experiments are conducted to demonstrate the helpfulness of 3D information to fingerprint recognition. Results show that a quick alignment can be easily implemented under the guidance of 3D finger shape feature even though this feature does not work for fingerprint recognition directly. The newly defined distinctive 3D shape ridge feature can be used for personal authentication with Equal Error Rate (EER) of ~8.3%. Also, it is helpful to remove false core point. Furthermore, a promising of EER ~1.3% is realized by combining this feature with 2D features for fingerprint recognition which indicates the prospect of 3D fingerprint recognition.

Anti-spoof touchless 3D fingerprint recognition system using single shot fringe projection and biospeckle analysis

Fingerprint is a unique, un-alterable and easily collected biometric of a human being. Although it is a 3D biological characteristic, traditional methods are designed to provide only a 2D image. This touch based mapping of 3D shape to 2D image losses information and leads to nonlinear distortions. Moreover, as only topographic details are captured, conventional systems are potentially vulnerable to spoofing materials (e.g. artificial fingers, dead fingers, false prints, etc.). In this work, we demonstrate an anti-spoof touchless 3D fingerprint detection system using a combination of single shot fringe projection and biospeckle analysis. For fingerprint detection using fringe projection, light from a low power LED source illuminates a finger through a sinusoidal grating. The fringe pattern modulated because of features on the fingertip is captured using a CCD camera. Fourier transform method based frequency filtering is used for the reconstruction of 3D fingerprint from the captured fringe pattern. In the next step, for spoof detection using biospeckle analysis a visuo-numeric algorithm based on modified structural function and non-normalized histogram is proposed. High activity biospeckle patterns are generated because of interaction of collimated laser light with internal fluid flow of the real finger sample. This activity reduces abruptly in case of layered fake prints, and is almost absent in dead or fake fingers. Furthermore, the proposed setup is fast, low-cost, involves non-mechanical scanning and is highly stable.

Study on novel Curvature Features for 3D fingerprint recognition

The human finger is a three-dimensional object. More information will be provided if 3D fingerprint images are available compared with 2D fingerprints. This paper explores 3D fingerprint features, as well as their possible applications. Novel fingerprint features, which are defined as Curvature Features (e.g. curve-skeleton, overall maximum curvatures), are for the first time proposed and investigated in this paper. Those features are then employed to assist more accurate fingerprint matching or classify human gender after analyzing their characteristics. A series of experiments are conducted to evaluate the effectiveness of employing these novel fingerprint features to fingerprint recognition based on the established database with 541 fingers. Results show that an Equal error Rate (EER) of ~15% can be achieved when only curve-skeleton is used for recognition. But, promising EER of ~3.4% is realized by combining curve-skeleton with classical 2D fingerprint features for recognition that indicates the prospect of 3D fingerprint recognition. The proposed overall maximum curvatures are found to be helpful for human gender classification.