# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
## 2 Prior Work  
In the fingerprint recognition literature, a variety of feature extraction algorithms have been proposed for identifying remarkable features. For example, in [11], [PERSON] and [PERSON] developed two computer vision algorithms (i.e., FAST and Harris) to extract features (i.e., corner points) from the finger vein image, where the fingerprint patterns are matched based on the differences between corners represented in the form of points using the Manhattan distance. The false match rate (FMR) and false non-match rate (FNMR) are then minimized to get the optimum threshold that triggers the final decision. The results of the study confirmed that the use of two adaptive algorithms concurrently reduces the error rate and helps to build a reliable system of finger vein identification. In [12], an improved Harris-SIFT image matching algorithm is proposed. First, the feature points of the image are extracted by the Harris corner detection operator. Then, the 28-dimensional increasing homocentric square window is applied to describe the neighborhood information of key feature points. Euclidean distance is typically employed as a similarity measure function in the matching process. Finally, simulation results demonstrated the validity of the improved algorithm, igniting a new thought for the research into the image matching. The fingerprint feature extraction process aims at finding various minutiae points in a fingerprint to use them further for fingerprint matching. However, the minutiae extraction process can be difficult and not accurate because a fingerprint image may be contaminated due to noise present in the fingerprint image. This results in a lot of minutiae point candidates. Thus, an optimal preprocessing technique is needed to minimize the number of key minutiae points and to get only those key points that can be further used to match the fingerprint [13]. In this direction, recently a study conducted by [PERSON] and [PERSON] [14] proposed a methodology for fingerprint minutiae extraction using morphological operations, where the number of minutiae points is calculated by using three different methods, and the result is then analyzed. The original image is compared against two preprocessed images; one is obtained by using a dilation operation and the other is obtained by first applying hole filling, followed by a dilation. In addition, [PERSON] et al. [15] proposed a new fingerprint feature detection algorithm, where it has been found that the presence of artifacts or noise in fingerprint images leads to a lot of spurious minutiae. To tackle this problem, a good strategy for feature extraction has been devised to extract the valid minutiae points in fingerprints and at the same time avoid extracting spurious minutiae points. The presented method could effectively perform the template matching [16] to find out bifurcation and termination of ridges. A smoothing algorithm is developed to find ridges in the fingerprint images with the help of eight different masks. It is a process of making a binary image of ridges from the grayscale fingerprint image. The experimental results verified the validity and accuracy of the algorithm in terms of genuine acceptance rate (GAR), false acceptance rate (FAR), and false rejection rate (FRR). Figure 1: Typical Minuutiae structures: (**a**) core and delta singularities; (**b**) ridge ending; and (**c**) ridge bifurcation. Furthermore, in [17], an algorithm based on Harris corner detector was proposed for extracting fingerprint minutiae. At the beginning, the Harris corner detector is applied to detect minutiae points and extract high curvature dots from the enhanced fingerprint image. In the later postprocessing stage, false or spurious minutiae are removed, based on the space distribution of minutiae. The type of minutiae is judged using a neighboring gray level information. The exact orientation of minutiae is decided by initial orientation and style.