文献翻译指纹识别系统Word文档下载推荐.docx

1、histogram equalization;ridge thinning;ridge ending;ridge bifurcation.1. INTRODUCTIONFingerprint recognition systems are termed under the umbrella of biometrics. Biometric recognition refers to the distinctive physiological （e.g. fingerprint, face, iris, retina） and behavioral（e.g. signature, gait） c

2、haracteristics, called biometric identifiers or simply biometrics, for automatically recognizing individuals. In 1893, it was discovered that no two individuals have same fingerprints. After this discovery fingerprints were used in criminal identification and till now fingerprints are extensively us

3、ed in various identification applications in various fields of life. Fingerprints are graphical flow-like ridges present on human fingers. They are fully formed at about seventh month of fetus development and fingerprint configuration do not change throughoutthe life except due to accidents such as

4、bruises or cut on fingertips.Because of immutability and uniqueness, the use of fingerprints for identification has always been of great interest to pattern recognition researchers and law enforcement agencies. Conventionally, fingerprint recognition has been conducted via either statistical or synt

5、actic approaches. In statistical approach a fingerprints features are extracted and stored in an n-dimensional feature vector and decision making process is determined by some similarity measures. In syntactic approach, a pattern is represented as a string, tree 1, or graph 2 of fingerprint features

6、 or pattern primitives and their relations. The decision making process is then simply a syntax analysis or parsing process.This paper suggests the statistical approach. Experimental results prove the effectiveness of this method on a computer platform, hence making it suitable for security applicat

7、ions with a relatively small database. The preprocessing of fingerprints is carried out using modified basic filtering methods which are substantially good enough for the purpose of our applications with reasonable computational time. Block diagram for the complete process is shown in Figure.1.2. IM

8、AGE PREPROCESSINGFor the proper and true extraction of minutiae, image quality is improved and image preprocessing is necessary for the features extraction because we cannot extract the required points from the original image. First of all, any sort of noise present in the image is removed. Order st

9、atistics filters are used to remove the type of noise which occurs normally at image acquisition. Afterwards the following image preprocessing techniques are applied to enhance the fingerprint images for matching.2.1 Histogram EqualizationThis method is used where the unwanted part of the image is m

10、ade lighter in intensity so as toemphasize the desired the desired part. Figure 2（a） shows the original image and Figure 2（b） histogram equalization in which the discontinuities in the small areas are removed. For the histogram equalization, let the input and the output level for an arbitrary pixel

11、be i and l, respectively. Then the accumulation of histogram from 0 to i （ 0 i 255,0 k 255） is given bywhere H（k） is the number of pixel with gray level k, i.e. histogram of an area, and C（i） is alsoknown as cumulative frequency. 2.2 Dynamic ThresholdingBasic purpose of thresholding is to extract th

12、e required object form the background. Thresholding is simply the mapping of all data points having gray level more that average gray level. The results of thresholding are shown in Figure 3.2.3 Ridgeline ThinningBefore the features can be extracted, the fingerprints have to be thinned or skeletonis

13、ed so that all ridges are one pixel thick. When a pixel is decided as a boundary pixel, it is deleted directly form the image 3-5 or flagged and not deleted until the entire image been scanned 6-7. There are deficiencies in both cases. In the former, deletion of each boundary pixel will change the o

14、bject in the image and hence affect the object symmetrically. To overcome this problem, some thinning algorithms use several passes in one thinning iteration. Each pass is an operation to remove boundary pixels from a given direction. Pavlidis 8 and Fiegin and Ben-Yosef 9 have developed effective al

15、gorithms using this method. However, both the time complexity and memory requirement will increase. In the latter, as the pixels are only flagged, the state of the bitmap at the end of the last iteration will used when deciding which pixel to delete. However, if this flag map is not used to decide w

16、hether a current pixel is to be deleted, the information generated from processing the previous pixels in current iteration will be lost. In certain situations the final skeleton may be badly distorted. For example, a line with two pixels may be completely deleted. Recently, Zhou, Quek and Ng 10 hav

17、e proposed an algorithm that solves the problem described earlier and is found to perform satisfactorily while providing a reasonable computational time. The thinning effect is illustrated in Figure 43. FEATURES EXTRACTIONThe two basic features extracted from the image are ridge endings and ridge bi

18、furcation. Forfingerprint images used in automated identification, ridge endings and bifurcation are referred to as minutiae. To determine the location of these features in the fingerprint image, a 3x3 window mask is used （Figure 5）. M is the detected point and X1 X8 are its neighboring points in a

19、clockwise direction. If Xn is a black pixel, then its response R （n） will be 1 or otherwise it will be 0. If M is an ending, the response of the matrix will bewhere R（9）=R（1）. For M to be a bifurcation,for example, if a bifurcation is encountered during extraction, mask will contain the pixelinforma

20、tion such as R（1） = R（3）= R（4） = R（6） = R（7） = 0, R（2） = R（5） = R（8） = R（9） = 1, andFor all the minutiae detected in the interpolated thinned image, the coordinates and their minutiae type is save as feature file. At the end of feature extraction, a feature record of the fingerprint is formed.4. MAT

21、CHINGFingerprint matching is the central part of this paper. The proposed technique is based on structural model of fingerprints 11. One of the major breakthroughs of this method is its ability to mach fingerprints that are shifted, rotated and stretched. This is achieved by a different matching app

22、roach. As it is clear that this algorithm matches the two fingerprint images captured at different time. This matching is based on the minutiae identification and minutiae type matching. Matching procedure is complex due to two main reasons;1） The minutiae of the fingerprint captured may have differ

23、ent coordinates2） The shape of the fingerprint captured at different time may be different due to stretching.An automated fingerprint identification system that is robust must have following criteria:1） Size of features file must be small2） Algorithm must be fast and robust3） Algorithm must be rotat

24、ionally invariant4） Algorithm must be relatively stretch invariantTo achieve these criteria, the structural matching method described by Hrechak and McHugh 11 is adopted as the basis of our recognition algorithm, with changes made to the algorithm, to provide more reliable and improving overall matc

25、hing speed. This matching represents the local identification approach, in which local identified features, their type and orientation is saved in features file, is correlated with the other images extracted features file. The model is shown in Figure 6.For each extracted features on the fingerprint

26、, a neighborhood of some specified radius R about the central feature is defined and then Euclidean distance and relative angles between the central point and the other point is noted with the points type. Since the distance among the pointremains the same throughout the life. So this technique work

27、s well for the rotated and shifted images.5. CONCLUSIONA fingerprint recognition algorithm that is fast, accurate and reliable has been successfully implemented. This algorithm can be modified, introducing the ridgeline count, and then could beused in online and real time automated identification an

28、d recognition system.REFERENCES1 MOAYER, B., and FU, K.S.: A tree system approach for fingerprint pattern recognition, IEEE Trans., 1986,PAMT-8, （3）, pp. 376-3872 ISENOR, D.K., and ZAKY, S.G.: Fingerprint identification using graph matching, Pattern Recognit., 1986,19, （2） pp. 113-1223 TAMURA, H.: A

29、 comparison of line thinning algorithms from digital geometry viewpoint. Proceedings of fourth international joint conference on Pattern Recognition, Kyoto, Nov. 1978, pp. 715-7194 HILDITCH, C.J.: Linear skeleton from square cupboards, Machine Intel., 1969, 4, pp.403-4205 NACCACHE, N.J., and SHINCHAL, R.: An investigation into the skeletonization approach of Hilditch,Pattern Recognit., 1984, 17, （3）, pp. 279-2846 JANG, B.K., and CHIN, P.T.: Analysis of thinning a