# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
## 4 Experimental Results and Analysis  
In this section, the results of the experiments and simulations conducted to verify the validity and performance of the proposed framework for fingerprint identification are presented. The experiments were carried out independently on a publicly available fingerprint database, namely FVC2004 [39] from the Fingerprint Verification Competition (FVC); a series of competition organised by the University of Bologna (Italy) which aims at establishing benchmarks for evaluating the performance of fingerprint recognition systems. FVC2004 is basically a multi-database, where each component database was created from fingerprints captured with a different sensor technology. In FVC2004 fingerprint database, there are four distinct datasets (i.e., DB1, DB2, DB3, and DB4), each containing 110 different fingerprint images with eight impressions per finger (resulting in a total of \(110\times 8=880\) fingerprints). Each component database is further divided into two disjoint subsets (A and B), so that the subset A contains the first 100 fingers and eight impressions per finger (i.e., 800 impressions in total), which is commonly used for the performance evaluation of fingerprint verification systems, whereas set B that is made available to allow parameter tuning before the submission of the algorithms only has the last 10 fingers (80 impressions). The fingerprints in FVC2004 database mainly depend upon image quality, size and the source sensor type used for acquiring the fingerprints. Fingerprint images are available in a TIFF format with 8-bit gray-level depth, and a resolution of about 500 dpi. The image size varies depending on the database. During performance evaluation, only homogeneous fingerprint images that belong to the same component database are matched to each other. Fingerprint samples taken from FVC2004 fingerprint database are shown in Figure 10.  
Taking into account that the fingerprint data included in FVC2004 database were collected by using different fingerprint sensing technologies, including optical, semiconductor, thermal, and tactile, and also the variations in image size and resolution are particularly apparent in this database. In addition, in this database, fingerprints were not acquired in realistic settings according to a formal protocol, but instead primarily characterized by the presence of different distortions (e.g., rotations, scalings, translations, and poor quality in resolution) within fingerprints of the same person's finger. Taking all these challenging aspects mentioned above into consideration, this dataset represents a great choice for the testing and validation of the proposed fingerprint matching framework in extreme conditions. Additionally, a publicly available fingerprint dataset presented in [40] was used for feature matching subsystem, where these fingerprint images acting as the fingerprint biometric data undergo minutiae points feature extraction. The performance of the proposed framework is evaluated on the FVC2004 benchmark fingerprint database (described above) by the Equal Error Rate (EER) that can be simply attained from the ROC (Receiver Operating Characteristic) curve; a plot of FMR against FNMR. The EER refers to the point on the ROC curve where FMR and FNMR are equal; a lower EER value generally indicates a better performance of the fingerprint verification  
Figure 10: A sample of fingerprint images from the FVC2004 fingerprint database. system. A plot of the ROC curve that quantitatively shows the performance of the proposed framework is presented in Figure 11. In real-world applications, a fingerprint recognition system usually operates away from the EER point by reducing the FMR in order to ensure a high level of security. Note that the FMR and FNMR are inversely related and there is a strict trade-off between them; therefore, it is of particular interest to evaluate how the FNMR is affected.