1、指纹识别算法的研究与实现-翻译苏州大学本科生毕业设计(论文)附件:外文文献资料与中文翻译稿 外文文献资料 收集:苏州大学 应用技术学院 10电子,学号1016405029, ,李磊, FINGERPRINT RECOGNITION USING MINUTIA SCORE MATCHING ABSTRACT: The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a persons life. A minutia matching is
2、 widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves
3、 the quality of the image and extract the minutiae from the thinned image. The false matching ratio is better compared to the existing algorithm. Key-words:-Fingerprint Recognition, Binarization, Block Filter Method, Matching score and Minutia. 1. Introduction Biometric systems operate on behavioral
4、 and physiological biometric data to identify a person. The behavioral biometric parameters are signature, gait, speech and keystroke, these parameters change with age and environment. However physiological characteristics such as face, fingerprint, palm print and iris remains unchanged through out
5、the life time of a person. The biometric system operates as verification mode or identification mode depending on the requirement of an application. The verification mode validates a persons identity by comparing captured biometric data with ready made template. The identification mode recognizes a
6、persons identity by performing matches against multiple fingerprint biometric templates. Fingerprints are widely used in daily life for more than 100 years due to its feasibility, distinctiveness, permanence, accuracy, reliability, and acceptability. Fingerprint is a pattern of ridges, furrows and m
7、inutiae, which are extracted using inked impression on a paper or sensors. A good 第 1 页 苏州大学本科生毕业设计(论文)附件:外文文献资料与中文翻译稿 quality fingerprint contains 25 to 80 minutiae depending on sensor resolution and finger placement on the sensor. The false minutiae are the false ridge breaks due to insufficient a
8、mount of ink and cross-connections due to over inking. It is difficult to extract reliably minutia from poor quality fingerprint impressions arising from very dry fingers and fingers mutilated by scars, scratches due to accidents, injuries. Minutia based fingerprint recognition consists of Thinning,
9、 Minutiae extraction,Minutiae matching and Computing matching score. Motivation: The motivation behind the work is growing need to identify a person for security. The fingerprint is one of the popular biometric methods used to authenticate human being. The proposed fingerprint verification FRMSM pro
10、vides reliable and better performance than the existing technique. Contribution: In this paper we used Fingerprint Recognition using Minutia Score Matching method with the help of MATLAB codes. Minutiae are extracted from the thinned image for both template and input image. Finally both the images a
11、re subjected to matching process and matching score is computed. Organization: This paper is organized into the following sections. Section II is an definition of the related work and describes Model for fingerprint recognition in detail, Section III gives the algorithm. In section IV performance an
12、alysis and results are discussed and finally in section V give the conclusions 2. Related work G. Sambasiva Rao et al., proposed fingerprint identification technique using a gray level watershed method to find out the ridges present on a fingerprint image by directly scanned fingerprints or inked36
13、impression. Robert Hastings developed a method for enhancing the ridge pattern by using a process of oriented diffusion by adaptation of anisotropic diffusion to smooth the image in the direction parallel to the ridge flow. The image intensity varies smoothly as one traverse along the ridges or vall
14、eys by removing most of the small irregularities and breaks but with the identity of the individual ridges and valleys preserved. Jinwei Gu, et al., proposed a method for fingerprint verification which includes both minutiae and model based orientation field is used. It gives robust discriminatory 第
15、 2 页 苏州大学本科生毕业设计(论文)附件:外文文献资料与中文翻译稿 information other than minutiae points. Fingerprint matching is done by combining the decisions of the matchers based on the orientation field and minutiae. V. Vijaya Kumari and N. Suriyanarayanan proposed a method for performance measure of local operators in fin
16、gerprint by detecting the edges of fingerprint images using five local operators namely Sobel, Roberts, Prewitt, Canny and LoG. The edge detected image is further segmented to extract individual segments from the image. Raju Sonavane, and B.S. Sawant presented a method by introducing a special domai
17、n fingerprint enhancement method which decomposes the fingerprint image into a set of filtered images then orientation field is estimated. A quality mask distinguishes the recoverable and unrecoverable corrupted regions in the input image are generated. Using the estimated orientation field, the inp
18、ut fingerprint image is adaptively enhanced in the recoverable regions. Eric P. Kukula, et al., purposed a method to investigate the effect of five different force levels on fingerprint matching performance, image quality scores, and minutiae count between optical and capacitance fingerprint sensors
19、. Three images were collected from the right index fingers of 75 participants for each sensing technology. Descriptive statistics, analysis of variance, and Kruskal-Wallis nonparametric tests were conducted to assess significant differences in minutiae counts and image quality scores based on the fo
20、rce level. The results reveal a significant difference in image quality score based on the force level and each sensor technology, yet there is no significant difference in minutiae count based on the force levels of the capacitance sensor. The image quality score, shown to be effected by force and
21、sensor type, is one of many factors that influence the system matching performance, yet the removal of low quality images does not improve the system performance at each force level. M. R. Girgisa et al., proposed a method to describe a fingerprint matching based on lines extraction and graph matchi
22、ng principles by adopting a hybrid scheme which consists of a genetic algorithm phase and a local search phase. Experimental results demonstrate the robustness of algorithm. Luping Ji, and Zhang Yi proposed a method for estimating four direction orientation field by considering four steps, i) prepro
23、cessing fingerprint image, ii) determining the primary ridge of fingerprint block using neuron pulse coupled neural network, iii) estimating block direction by projective distance variance of a ridge, instead of a full block, iv) correcting 第 3 页 苏州大学本科生毕业设计(论文)附件:外文文献资料与中文翻译稿 the estimated orientat
24、ion field. Duoqian Maio et al., used principal graph algorithm by kegl to obtain principal curves for auto fingerprint identification system. From principal curves, minutiae extraction algorithm is used to extract the minutiae of the fingerprint. The experimental results shows curves obtained from g
25、raph algorithm are smoother than the thinning algorithm. Alessandra Lumini, and Loris Nanni developed a method for minutiae based fingerprint and its approach to the problem as two - class pattern recognition. The obtained feature vector by minutiae matching is classified into genuine or imposter by
26、 Support Vector Machine resulting remarkable performance improvement Xifeng Tong et al., proposed a method to overcome non linear distortion using Local Relative Error Descriptor (LRLED).The algorithm consists of three steps i) a pair wise alignment method to achieve fingerprint alignment ii) a matc
27、hed minutiae pair set is obtained with a threshold to reduce non-matches finally iii) the LRLED based similarity measure. LRLED is good at distinguishing between corresponding and non corresponding minutiae-pairs and works well for fingerprint minutiae matching. Lam et al., presented a method, thinn
28、ing is the process of reducing thickness L. of each line of patterns to just a single pixel width. The requirements of a good algorithm with respect to a fingerprint are i) the thinned fingerprint image obtained should be of single pixel width with no discontinuities ii) Each ridge should be thinned
29、 to its central pixel iii) Noise and singular pixels should be eliminated iv) no further removal of pixels should be possible after completion of thinning process. Mohamed et al., presented fingerprint classification system using Fuzzy Neural Network. The fingerprint features such as singular points
30、, positions and direction of core and delta obtained from a binarised fingerprint image. The method is producing good classification results. Ching-Tang Hsieh and Chia-Shing Hu 14 has developed anoid method for Fingerprint recognition. Ridge bifurcations are used as minutiae and ridge bifurcation al
31、gorithm with excluding the noiselike points are proposed. Experimental results show the humanoid fingerprint recognition is robust, reliable and rapid. Lie Wei proposed a method for rapid singularities searching algorithm which uses delta field Poincare index and a rapid classification algorithm to
32、classify the fingerprint in to 5 classes. The detection algorithm searches the direction field which has the larger direction changes to get the singularities. Singularities 第 4 页 苏州大学本科生毕业设计(论文)附件:外文文献资料与中文翻译稿 detection is used to increase the accuracy. Hartwig Fronthaler, et al., Proposed fingerpr
33、int enhancement to improve the matching performance and computational efficiency by using an image scale pyramid and directional filtering in the spatial domain. Mana Tarjoman and Shaghayegh Zarei introduced structural approach to fingerprint classifications by using the directional image of fingerprint instead of Ravi.J. et
