Image Processing 352-567-1-SM.txt

7/27/2019 Image Processing 352-567-1-SM.txt

1/5

JurnalTeknologiInformasi dinamikvolume16, No. 1,January 2011 :: 64-71 ISSN :: 0854-9524pengolahcitra Digital untukidentifikasi characteristicsBased sidikjari Minutiaeekaardhianto, sitimunawarohdanagung Prihandono fakultasteknologiinformasi, University of Stikubank email:[email protected] , [email protected] , [email protected], including the fingerprint recognition, commonly used to identify andverification. Biometricrecognition is recognition or identify a person based onspecific biological characteristics which is owned by that person. Every personhas a set of fingerprints is unique consists of patterns-dark lines from the skinthat's socalled ridge (ridges). Each-each patterns ridge produce a form patterns different areas. In this research done processing picture fingerprint that were obtained the pattern anatomy riges fingerprint that could be used as identifikator one. The activity that is to make acquisition process image, grayscalling,thresholding, thinning (skeletonizing) and patern matching. The findings suggestis that picture fingerprints could be identified patterns ridgesnya and from each picture fingerprint produce a way different from sehinggadapat used sebagaialatidentifikasi. Katakunci:Biometric,fingerprintidentification, Image ProcessingINTRODUCTION is different. Processing picture or image processing is often called image processing, Biometrics, including in it is a process that change a fingerprint recognition, in general used images to other pictures that for the identification and verification have. The identification would be better quality. A pe

rson's identity is knowing, will be a comparison between songwriters biometric data BIOMETRIKSIDIK FINGER someone in the database contains fingerprint record characters use is one person. The pattern biometrics good thing to do Biometric recognition is authentication system testing data. Picture 1 knowledge or identification someone's fingerprints explained that the use according to the characteristics specific biological (fingerprint) will give more owned by people. Its function between akuratdarimayoritassistembiometrikyanglain, another is to securitysystem with also did not need more cost) mengenaliidentitas one. Dalampembuatannya. Biometric identification Technology offers biological that use the characteristics which is owned by allowing individual system to be able to identify usersin the right place, for example, iris (iris), fingerprint (fingerprint), pattern hands (hand), a signature (signature), face (face) and the voice (voice). Every person has a set of fingerprints comprised of some patterns unique Picture 1.

Comparison Graphics system bio--line line darkness from the skin's socalled metric(source:http://scgwww.efpl.ch.courses) pitch (ridges). Each-each patterns ridge produce a form patterns area64 processor Digital image to Identify The Fingerprint Based Minutiae

jurnalteknologiinformasi dinamikvolume16, No. 1,January 2011 :: 64-71 ISSN :: 0854-9524Every person has a set of fingerprints is unique consists of patterns-dark lines from the skin that's socalled ridge (ridges) that is shown as white and the line-bright darikulit yangturun called wrinkles (furrows) That is shown as color Pict

ure 3. Macamukurankarakteristikanatomi dark in the picture 2(a). Ridge Area line pitch sometimes-sometimes DIGITAL IMAGE area known as the pattern. Each-each patterns ridge produce a pattern form a picture is defined as different areas. Central figure finger mencer-minkan figure that was found in the field dwi gili matra patterns area, known as the core basic core point). Or two dimensions. Accordingto Munir (2004), in The first moment pen-cabangan pattern or double mathematical picture is a function and anatomy at deviation of the two shape of the coastline(continue) from the intensity in the field called delta. Cam fingerprint that twodimensional. A picture of digitalterbentuk cut off socalled ujungbubungan. The point forming the picture that is called denganpixel(pixel). Picture 2(b) sho


2/5

wed form delta sebagaibentukgaris branching(bifurcation). Ahmad (2005) said that pixel camera is a sample of the scenery that contains the intensity light thatis stated in the number round. So digital image is a collection pixel camera that dinotasikan grows in form numbers in some lines and fields. Pictures that referred to in writing is "the picture shut up" the picture single (a) (b) that donot move. For the next picture diamakandisebut with picture. Picture 2.Specimensidikjari. (A) scanning result(b) hasilpengolahancitra PROCESSING DIGITAL PICTURES Based on pitch, according to processing picture or image processing research done by Syamsa (2004) that is often called image processing, varying sizes anatomical characteristics ridge is a process that change a adalahsepertiterlihat in Figure 3. Picture to other pictures that have quality lebihbaikuntuktujuantertentu. As an example is the image 2 (a) is the image fingerprints taken directly, but to know the features fingerprint picture early mixed as can be seen in the picture 2 (b) to be process lebihlanjut. In essence, Image processing modify each pixel in the picture in accordance with demand. Operation images that are used inthe writing is grayscalling, floating (thresholding),thinning(depletion) dantemplate matching.Digital image processor for Fingerprint Identification featureBased Minutiae 65

jurnalteknologiinformasi dinamikvolume16, No. 1,January 2011 :: 64-71 ISSN :: 0854-9524

GRAYSCALLING Grayscalling is a process simplification picture of picture formatcolor RGB to picture is gray- ........................ Benefits (4) abu (gray). Apicturecolored RGB functions (2.3) and (2.4) (Munir, 2004) can have three layers matrixes namely R-layer, G- explained that the pixel (x,y) from the picture layerand B- layer. If every process hasilf'(x,y) akanbernilai0 if nilaipiksel(x,y) calculations are made in each layer, picture early f(x,y) less than the threshold one pixel camera will be charged with three times operation, (T) and The pixel (x,y) result from the picture so that the concept three layer RGB simplified f'(x,y) will valuable 1 if the pixel (x,y) to a layer that is a layer grayscale or monochrome documents at input resolutions. Picture awalf(x,y) menyamaiatau than Tochange color picture that the threshold (T), this also applies to have the matrixes each-each R, G instead. The method Otsu (Otsu, 1979) and B to their grayscaleimages with the k, count the threshold (T) automatically and conversion can be

done with based on a picture inputs. An approach that take price-price from the R,G and B used by the methods Otsu is with (Ahmad, 2005) that will be easy to doan analysis diskriminan namely dituliskansepertipersamaan(2.1). To determine avariables that can be k= ( R+ G+ B)/3 ............................... (1) distinguish between the two or more groups that appeared in a natural way. Analysis ofDiskriminan Ahmad (2005) wrote that will maximize variables that because the three colors R, G and B is considered to be not be able to separate objects with the background. Uniform in the significant contribution For example the thresholdthat will be sought to brightness, some hold declared in k. The k is between 1that the conversion more precise use to L, with L = 255. Probability similarities(2.2). Untukpikseli dinyatakandengan: k= (0,299R+ 0,587G +0.114 B) .............. (2) FLOATING OTSU ........................ PRAMBON (5) (OTSUTHRESHOLDING) with ni said that the number of pixel camera with Floating or thresholding high ke

abuan i, and N said that number is one of the ways do pixel in the picture. Themoment to cumulative- segmentation picture or pemisahkan picture zero, it was a moment to cumulative-one, and the average- group-group that represents a row average-alsocan be declared as a region. The process segmentation picture below: by using thresholding in dasaranya is separated the pixel images based on a or some values......................................... (6) limits (threshold). In writing this picture fingerprint disegmentasikan to two areas, ......................................... (7) that the region ridge and the furrows, so images will only have one the threshold (T) with two different colors ........................ .................. (8) that is white and black or identical with the threshold k 0 c


3/5

an be determined by dan1. Memaksimumkanpersamaan:........................ Benefits (3) or ...................... (9)66 processor Digital image to Identify The Fingerprint Based Minutiae

jurnalteknologiinformasi dinamikvolume16, No. 1,January 2011 :: 64-71 ISSN :: 0854-9524to calculate the threshold (T), first peel pixel the outside of objects, the form is to make histogram notation from the picture's matrix 3 x 3 can be shown inthe picture that is meant by adding the k 3.5. Mask depletion done repeatedly ifthere are pixel camera that has period starting from early position picture until the end with level (L), in this level (L) is the image denganlangkahsebagaiberikut : format with level for images grayscale or monochrome documents at inputresolutions 1. Marking the P8 as the point that will be starting from 0 (zero) up to 255. Be removed, when P8 to meet the requirements is counting nilaiprobabilitas below: a. ( 2 N (Pt) 6 ) and ( S(Pt) = 1 for every level (L) by comparing )with the assumption that the value P8 = 1, N(Pt) is examine number of pixel ata level with the total number of neighbor of P8 which is equivalent pixel in thepicture or can be rendered as the P8 formulated as equal to the similarities (2.5). Then for each (2.10 ) and the S(Pt) is the number of transition from a number of the moment to cumulative- 0 to 1 in the rear P0 + P1 + ... + P6 + 0 with equality (2.6), it was a moment to cumulative- P7. 1 With equality (2.7) and the ave

rage-price with persaman (2.8) which N(Pt) =P0+P1+ ... +P6 +P7(2.10 ) counted thevariant with equality (2.9). 2. Remove all the point that was marked in If alllevels have been counted, and for the next langkah1. The most sought after the variant that was to digunakansebagainilaiambang(T). 3. Repeat steps 1 and 2 until there is no lagiyang removed. SKELETONIZING (THINNING) 4. Simpanhasilakhir sebagaigambar new. An approach representation structure of an object is to reduce to a graph (Gonzallez and Woods, 1993). Graph that is meant is form an object that more thin or a picture 4 line. Notasimaskalgoritma depletion framework (butterworth had) which is produced from algorithm thinning (ozone) or often called the TEMPLATE MATCHING skeletonizing. Template matching algorithm is the depletion(thinning algorithm) (Davies and end in searching for basic characteristics ridge fingerprints. Plummer, 1980) is a process repeatedly techniques templates matching based pattern (recuring) that are intended to remove is best known and most

technical or reduce part of an object that not many used (Budiman, 2006). Theneed to that produced only information that this approach, his/her fingerprintswould be described as essential to facilitate the process vector lines with wideone pixel of form. In Munir (2004), Pitas and ridge branching (bifurcation) andthe Ioannis algorithm that ozone pitch (end point) as shown in objects must meet the requirements as a figure 5(Budiman, 2006). The following: 1. Maintainingconnection pixel pixel objects. In other words, not cause form objects to be broken. 2. Not lessen the arms of bentukyang is then pressed into thin shapes. Depletion algorithm (Gonzallez and Woods, 1993) use mask (matrixes) with the 3 x 3 that at this writing will Picture 5.Corakujungdanpercabangan referred to as mask,and masking ridge(ridge)Digital image processor for Fingerprint Identification featureBased Minutiae 67

jurnalteknologiinformasi dinamikvolume16, No. 1,January 2011 :: 64-71 ISSN :: 0854-9524Coraksidik jaridinotasikan with matrixes PLOT or (mask) with the 3 x 3. To typeridge end Design the process inibertujanuntuk point, there are 8 pattern mask ascan be shown to facilitate understanding to the way in the picture (6), whilefor the pattern ridge research. Process of identification Sidikjari will bifurcation there are 12 patterns mask that through several stages the acquisition sidi


4/5


5/5

Of the table 3, it can be said that every sidikjari that he had made a sample have anatomiriges differently. Next is test using images scales fingers that period, but be changes in the picture is that play picture, so the result is as can be seen from the tabel4. Table4. HasilUjiCoba kecocokansidikjari

jurnalteknologiinformasi dinamikvolume16, No. 1,January 2011 :: 64-71 ISSN :: 0854-9524From the table 4, it can be seen that picture sidik Binarization, Patern Recognition, No. 39, which will be done changes will be 89-101, www.elvesier.com /locate/patcog produces the anatomy that is a little different. Gonzales, R. C. , Woods,R. E. , 1993, Digital is seen as an example is a no_005 Image Processing, Wesley Publishing has the combination anatomy 185, 91, 5 and Company, USA. No_005_r fingerprint is the image of the First Secretary rotated 10/that produce Nobuyuki,O. , 1979, A Threshold Selection kombinasianatoi183, 91, 6. Method From Gray-Level Histograms, IEEE Trans. Syst. Man Cybern, this. 62- CONCLUSION 66. From the research has been done, then Putra, D. ,2004, Binerisasi image hand in hand with canbe taken some kesimpulanyaitu : methods Otsu, Technology Journal Electrical Engineering 1. The image processing techniques which will be done.' Sudjito Udayana University, No. 2, can recognize anatomy shape riges sidik Vol. 3, This. 11-13.Finger. Rinaldi Munir, "image processing Digital 2. Each sidikjari patterns combination with the approach algorithmic graphic", anatomy that is different from

that can Informatics Bandung, 2004 is used as one of the ways Suyanto, A. H. , 2005, Reviews pengidentifikasianseseorang methodology. Software Development, QUESTION and www.asep_hs.web.ugm.ac.id To improve and process further research thatwas proposed suggestions as follows: 1. Fingerprints on each person should be taken more than onetime fingerprint, because a shift in the picture may lead to perubahanciri data ridge are processed. 2. In the decisionmaking process samplessidikjari is expected to use the equipment scanner in accordance with anatomijarimanusia form. List of LIBRARIES Ahmad, the U.K. , 2005, pengolahancitra digitaldan Tekik Pemrogramannya, Graha Knowledge, Yogyakarta.Ardisasmita, M. S. , 2004, Development of the model for mathematical analysis ofFingerprint Identification System Automatically, Computing in the field of science and teknolog i nuclear Technology Development Center XII, Informatics and Computing, Atomic Energy Agency. Blyvas, I. , Bruckstein, A. , Kimmel, R. , 2005,

Efficient Computation of Adaptive Threshold Surfaces for ImageProcessor Digital image to Identify The Fingerprint Based Minutiae 71

Image Processing 352-567-1-SM.txt

Documents

Transcript of Image Processing 352-567-1-SM.txt