Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

From the privacy perspective most concerns arise from the storage and misuse of biometric data (Cimato et al., 2009). ... is provided with a in-depth discussion of the state-of-the-art in iris biometric cryptosystems, which completes this work

Enhanced Fuzzy Feature Match Algorithm for Mehndi Fingerprints

The performance of biometric system is degraded by the distortions occurred in finger print image acquisition. This paper focuses on nonlinear distortions occurred due to �Mehndi / Heena drawn on the palm/fingers. The present invention is to detect and rectify such distortions using feedback paradigm. If image is of good quality, there is no need to renovate features. So, quality of whole image is checked by generating exponential similarity distribution. Quality of local region is checked by the ridge continuity map and ridge clarity map. Then, we check whether feedback is needed or not. The desired features such as ridge structure, minutiae point, orientation, etc. are renovated using feedback paradigm. Feedback is taken from top K matched template fingerprints registered in the database. Fuzzy logic handles uncertainties and imperfections in images. For matching, we have proposed the Enhanced Fuzzy Feature Match (EFFM) for estimating triangular feature set of distance between minutiae, orientation angle of minutiae, angle between the direction of minutiae points, angle between the interior bisector of triangle and the direction of minutiae, and a minutiae type. The proposed algorithm incorporates an additional parameter minutiae type that assists to improve accuracy of matching algorithm. The experimentation on 300 Mehndi fingerprints acquired using Secugen fingerprint scanner is conducted. The results positively support EEFM for its efficiency and reliability to handle distorted fingerprints matching

Estimation of the Precision of a Structured Light System in Oil Paintings on Canvas

[EN] The conservation and authentication of pictorial artworks is considered an important part of the preservation of cultural heritage. The use of non-destructive testing allows the obtention of accurate information about the state of pictorial artworks without direct contact between the equipment used and the sample. In particular, the use of this kind of technology is recommended in obtaining three-dimensional surface digital models, as it provides high-resolution information that constitutes a kind of fingerprint of the samples. In the case of pictorial artworks with some kind of surface relief, one of the most useful technologies is structured light (SL). In this paper, the minimum difference in height that can be distinguished with this technology was estimated, establishing experimentally both the error committed in the measurement process and the precision in the use of this technology. This study focused on the case of oil paintings on canvas and developed a low-cost system to ensure its wide use.Sánchez-Jiménez, D.; Buchón Moragues, FF.; Bravo, JM.; Sánchez Pérez, JV. (2019). Estimation of the Precision of a Structured Light System in Oil Paintings on Canvas. Sensors. 19(22):1-13. https://doi.org/10.3390/s19224966S1131922Abate, D., Menna, F., Remondino, F., & Gattari, M. G. (2014). 3D painting documentation: evaluation of conservation conditions with 3D imaging and ranging techniques. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5, 1-8. doi:10.5194/isprsarchives-xl-5-1-2014Pelagotti, A., Uccheddu, F., Massa, E., & Carfagni, M. (2018). Comparing two 3D measurement techniques for documenting painted wooden panels surface deformations on a real test case: «Mystical Marriage of Saint Catherine» by Renaissance artist Piero di Cosimo. IOP Conference Series: Materials Science and Engineering, 364, 012090. doi:10.1088/1757-899x/364/1/012090Ambrosini, D., Daffara, C., Di Biase, R., Paoletti, D., Pezzati, L., Bellucci, R., & Bettini, F. (2010). Integrated reflectography and thermography for wooden paintings diagnostics. Journal of Cultural Heritage, 11(2), 196-204. doi:10.1016/j.culher.2009.05.001Legrand, S., Vanmeert, F., Van der Snickt, G., Alfeld, M., De Nolf, W., Dik, J., & Janssens, K. (2014). Examination of historical paintings by state-of-the-art hyperspectral imaging methods: from scanning infra-red spectroscopy to computed X-ray laminography. Heritage Science, 2(1). doi:10.1186/2050-7445-2-13Bravo, J., Sánchez-Pérez, J., Ferri, M., Redondo, J., & Picó, R. (2014). Application of Ultrasound Phase-Shift Analysis to Authenticate Wooden Panel Paintings. Sensors, 14(5), 7992-8002. doi:10.3390/s140507992Remondino, F., Rizzi, A., Barazzetti, L., Scaioni, M., Fassi, F., Brumana, R., & Pelagotti, A. (2011). Review of Geometric and Radiometric Analyses of Paintings. The Photogrammetric Record, 26(136), 439-461. doi:10.1111/j.1477-9730.2011.00664.xBuchón-Moragues, F., Bravo, J., Ferri, M., Redondo, J., & Sánchez-Pérez, J. (2016). Application of Structured Light System Technique for Authentication of Wooden Panel Paintings. Sensors, 16(6), 881. doi:10.3390/s16060881Tian, G. Y., Lu, R. S., & Gledhill, D. (2007). Surface measurement using active vision and light scattering. Optics and Lasers in Engineering, 45(1), 131-139. doi:10.1016/j.optlaseng.2006.03.005Secher, J. J., Darvann, T. A., & Pinholt, E. M. (2017). Accuracy and reproducibility of the DAVID SLS-2 scanner in three-dimensional facial imaging. Journal of Cranio-Maxillofacial Surgery, 45(10), 1662-1670. doi:10.1016/j.jcms.2017.07.006Luhmann, T. (2010). Close range photogrammetry for industrial applications. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 558-569. doi:10.1016/j.isprsjprs.2010.06.003Hui, Z., Liyan, Z., Hongtao, W., & Jianfu, C. (2009). Surface Measurement Based on Instantaneous Random Illumination. Chinese Journal of Aeronautics, 22(3), 316-324. doi:10.1016/s1000-9361(08)60105-3McPherron, S. P., Gernat, T., & Hublin, J.-J. (2009). Structured light scanning for high-resolution documentation of in situ archaeological finds. Journal of Archaeological Science, 36(1), 19-24. doi:10.1016/j.jas.2008.06.028Arias, P., Herráez, J., Lorenzo, H., & Ordóñez, C. (2005). Control of structural problems in cultural heritage monuments using close-range photogrammetry and computer methods. Computers & Structures, 83(21-22), 1754-1766. doi:10.1016/j.compstruc.2005.02.018Patrucco, G., Rinaudo, F., & Spreafico, A. (2019). A NEW HANDHELD SCANNER FOR 3D SURVEY OF SMALL ARTIFACTS: THE STONEX F6. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 895-901. doi:10.5194/isprs-archives-xlii-2-w15-895-2019Guidi, G., Atzeni, C., Seracini, M., & Lazzari, S. (2004). Painting Survey by 3D Optical Scanning - The Case ofAdoration of the Magiby Leonardo Da Vinci. Studies in Conservation, 49(1), 1-12. doi:10.1179/sic.2004.49.1.1Palma, G., Pingi, P., Siotto, E., Bellucci, R., Guidi, G., & Scopigno, R. (2019). Deformation analysis of Leonardo da Vinci’s «Adorazione dei Magi» through temporal unrelated 3D digitization. Journal of Cultural Heritage, 38, 174-185. doi:10.1016/j.culher.2018.11.001Batlle, J., Mouaddib, E., & Salvi, J. (1998). Recent progress in coded structured light as a technique to solve the correspondence problem. Pattern Recognition, 31(7), 963-982. doi:10.1016/s0031-3203(97)00074-5Salvi, J., Pagès, J., & Batlle, J. (2004). Pattern codification strategies in structured light systems. Pattern Recognition, 37(4), 827-849. doi:10.1016/j.patcog.2003.10.002DAVID SLS-1 FLYER http://kvejborg.dk/media/1245/flyer-web.pdfCloudCompare (Version 2.6) [GPL Software] http://www.cloudcompare.org/Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239-256. doi:10.1109/34.121791Gold, S., Rangarajan, A., Lu, C.-P., Pappu, S., & Mjolsness, E. (1998). New algorithms for 2D and 3D point matching. Pattern Recognition, 31(8), 1019-1031. doi:10.1016/s0031-3203(98)80010-1Bing Jian, & Vemuri, B. C. (2011). Robust Point Set Registration Using Gaussian Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1633-1645. doi:10.1109/tpami.2010.223Oomori, S., Nishida, T., & Kurogi, S. (2016). Point cloud matching using singular value decomposition. Artificial Life and Robotics, 21(2), 149-154. doi:10.1007/s10015-016-0265-xZhu, H., Guo, B., Zou, K., Li, Y., Yuen, K.-V., Mihaylova, L., & Leung, H. (2019). A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration. Sensors, 19(5), 1191. doi:10.3390/s19051191Lin, C.-C., Tai, Y.-C., Lee, J.-J., & Chen, Y.-S. (2017). A novel point cloud registration using 2D image features. EURASIP Journal on Advances in Signal Processing, 2017(1). doi:10.1186/s13634-016-0435-yAbate, D. (2019). Documentation of paintings restoration through photogrammetry and change detection algorithms. Heritage Science, 7(1). doi:10.1186/s40494-019-0257-

A new algorithm for minutiae extraction and matching in fingerprint

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A novel algorithm for fingerprint template formation and matching in automatic fingerprint recognition has been developed. At present, fingerprint is being considered as the dominant biometric trait among all other biometrics due to its wide range of applications in security and access control. Most of the commercially established systems use singularity point (SP) or ‘core’ point for fingerprint indexing and template formation. The efficiency of these systems heavily relies on the detection of the core and the quality of the image itself. The number of multiple SPs or absence of ‘core’ on the image can cause some anomalies in the formation of the template and may result in high False Acceptance Rate (FAR) or False Rejection Rate (FRR). Also the loss of actual minutiae or appearance of new or spurious minutiae in the scanned image can contribute to the error in the matching process. A more sophisticated algorithm is therefore necessary in the formation and matching of templates in order to achieve low FAR and FRR and to make the identification more accurate. The novel algorithm presented here does not rely on any ‘core’ or SP thus makes the structure invariant with respect to global rotation and translation. Moreover, it does not need orientation of the minutiae points on which most of the established algorithm are based. The matching methodology is based on the local features of each minutiae point such as distances to its nearest neighbours and their internal angle. Using a publicly available fingerprint database, the algorithm has been evaluated and compared with other benchmark algorithms. It has been found that the algorithm has performed better compared to others and has been able to achieve an error equal rate of 3.5%

A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation

Intrinsic isometric shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency, i.e., the metric structure of the whole manifold must not change significantly. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise (incomplete data and contacts), which is a common problem in real-world 3D scanner data. In this paper, we introduce a new approach to partial, intrinsic isometric matching. Our method is based on the observation that isometries are fully determined by purely local information: a map of a single point and its tangent space fixes an isometry for both global and the partial maps. From this idea, we develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent spaces. From this, we derive a local propagation algorithm that find such mappings efficiently. In contrast to previous heuristics based on RANSAC or expectation maximization, our method is based on a simple and sound theoretical model and fully deterministic. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous heuristic partial matching algorithms.Comment: 17 pages, 12 figure