# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 2 Related Work  
The past two decades or so have witnessed the development of many biometric technologies based on fingerprint matching. [PERSON] [7] investigated the problem of reducing the fingerprint features fed into the neural network, where the input samples of the back-propagation method are coded and normalized using an algorithm worked with a prepared codebook. The key advantages of preparing a codebook lie in the simplicity of its idea and its high-speed processing. The method was tested on the FCV2002 fingerprint database, achieving a recognition accuracy of 94% with an equal error rate (EER) of 2.1. In [8], the authors proposed a fingerprint recognition system composed of several stages, i.e., pre-processing for image enhancement, binarization and thinning of fingerprint images, feature extraction from the thinned image ridge, and finally the matching stage in which similarity and distance measurements are used to match two minutiae points. The recognition method was evaluated accurately and reliably by using fingerprint images from FCV2000 and FVC2002 databases, achieving an accuracy of 98.55% with a false acceptance rate (FAR) and a false rejection rate (FRR) of 0.0154 and 0.0137, respectively. Furthermore, in [9], [PERSON] and [PERSON] introduced an innovative modification to one of the most powerful algorithms developed to extract the points of interest (POIs), called SURF. These key points, also called features of interest, are later used in the matching process after the outlier sets are eliminated. The presented system was experimentally evaluated on FVC2002 DB1 and FVC2004 DB2 fingerprint databases, using a set of performance evaluation metrics such as accuracy, FRR, FAR, EER, and SMR. The obtained results showed that the system was capable of successfully finding a (1:1) match between one fingerprint and another, as well as a (1:M) match when more than one fingerprint image is compared with the query image. Additionally, in [10], [PERSON] et al. developed a fingerprint recognition system where a piece of security system for identity verification is presented based on unique features extracted from each fingerprint to differentiate one from others. In their work, the detection of minutia coordinates and ridge orientation are utilized to determine uniqueness and do matching. Through MATLAB simulations, it was verified that the developed algorithm could effectively achieve a high recognition rate with an accuracy up to 96.11%. In [11], an algorithm was presented to identify the valid minutiae and increase the accuracy level and acceptance rate. The developed algorithm is split into two major steps: pre-processing and post-processing. Initially, the algorithm performs appropriate pre-processing steps to remove noise and obtain a noise-free fingerprint image for feature extraction. Then, among the extracted minutiae, false minutiae are eliminated in later post-processing stages to extract exact core point detection and matching valid minutiae. The algorithm performance was evaluated on FVC2000 and FingerDOS databases, achieving an average FMR of 1%, FNMR of 1.43%, and accuracy of 98.7%. Moreover, [PERSON] [12] introduced a new fingerprint matching method based on fingerprint orientation analysis utilizing local binary patterns (LBP) obtained from the ridge orientation field. The maximization of mutual information between orientation features extracted from fingerprint images is used to align the images. The fingerprint image is cropped around the detected reference point to locate the region of interest (ROI). The Chi-square test, Euclidean distance, and least square support vector machine (LSSVM) were used to evaluate the matching performance of the orientation local binary pattern (OLBP) descriptor on the public FVC2002, FVC2004, and FVC2006 fingerprint datasets. The results obtained in this study indicated that the performance of LBP features extracted from the orientation image is comparable to those previously reported in the literature.