Evaluation of Fingerprint Identification Based on Local Binary Pattern (LBP)

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1 Evaluatio of Figerprit Idetificatio Based o Local Biary Patter (LBP) Ashok T. Gaikwad Istitute of Maagemet Studies ad Iformatio Techology, Auragabad, (M.S) 4311, Idia *Correspodig Author: drashokgaikwad@gmail.com Abstract -this paper was aalyzed ad evaluatio Ui-modal biometric system based o figerprit idetificatio system. The system was covered may stages to remove the oise from the figerprit image by usig ehacemet techiques. The feature extractio was performed by usig a popular texture feature which called Local Biary Patter (LBP). The matchig process was doe by comparig the test with a template by usig distace measure Euclidea distace. The decisio was performig with help of threshold values to decide the perso to idetify or ot idetify. The system was tested o four datasets ad evaluated o each dataset idividually ad fially, the compariso betwee all the dataset was preset by differet evaluatio parameter. The results show that FVC2 gave more efficiecy ad best result as compare with other datasets with a maximum GAR of 96.15% with miimum EER of 3.85% at threshold values.2 Keywords Figerprit, Idetificatio, Local Biary Patter, aalysis I. INTRODUCTION Recetly, moitorig ad security have become a essetial ad importat affair because the umber of couterfeiters ad hacker are icreased for the covetioal methods like Persoal Idetificatio Number (PIN) ad passwords. The traditioal methods suffer from some type of cotravetios ad breaches for example the uauthorized user ca arrive to importat data i a dedicated system to delete, chage, or eve steal it. For avertig whole these cocers; the moder commuity directs to more credibility methods recetly utilize the biometric-techologies. Biometrics provides more secure way of perso autheticatio, they are difficult to be stole ad replicated. Biometrics method ca be depicted as a automate techique to recogize perso automatically based o his or her behavioral ad/or physiological features. This techology has possessed a big amout of cocer ad care for security i almost all aspects of our daily life sice perso caot forget or lose their physiological features i the way that they might lose password or a idetity card [1]. Biometric techologies have bee developed for automatic recogizig of huma idetity depedig o perso special biological features, such as face, Iris, speech ad figerprit. The olie security of autheticatio systems is ot oly a substitutio of secret codes ad passwords, but it is also related to securig ad moitorig the system i differet level of potetial applicatios [2]. May biometric systems are ow curretly used i may applicatios like attedace i educatioal istitutios, idustries ad hospitals, security check at airports, ATMs ad access to importat documets i baks. Nowadays the biometric data is beig collected from all idividuals across the coutry to maitai a digital record of its populatio. The data thus collected by may coutries are also beig used to provide passports cotaiig digitized biometric data like face, figerprits, iris ad sigature. Thus with icreased demad i biometric applicatio may biometric systems are beig developed. But it is foud that eve advaced biometric systems are facig a lot of problems i terms of data collectio, algorithms developed ad system desig. Noisy data, o -uiversality, user acceptace ad spoof attacks are some of the challeges ecoutered i desigig a biometric system [3]. Though biometric systems are developed to solve these problems, some of the algorithms suffer from high computatioal complexity, more executio time, less recogitio rate ad high error rates. Recetly multimodal biometrics is desiged to icrease the performace of the biometric system. The performace of such multimodal systems depeds o how effectively it is possible to combie the iformatio from differet biometric characteristics. Huma idetificatio [4] results i mutual trust which is essetial for proper societal fuctioig. Idetifyig fellow humas has bee doe based o voice, appearace or gait for years. A scietific ad systematic basis for huma idetificatio bega i the 19th cetury whe Alphose Bertillo itroduced the use of may athropomorphic measuremets to idetify habitual crimials. The article was orgaized with differet sectio the remaiig sectio was literature survey i the sectio II. The methodology of the system was covered i the sectio III. I the sectio IV coducted with Results ad discussio. Fially, the coclusio ad future work o sectio V. II. RELATED WORK Yog ad park, 28[5] proposed the figerprit verificatio usig ivariat momet ad a eural etwork. Ad they used STFT for preprocessig ad LMS algorithm for orietatio ad ivariat momet aalysis o ROI. The matchig stage implemeted by similarity measures usig absolute distace ad BPNN. They are resulted the fast matchig speed ad high matchig accuracy compare with 395 IJREAMV4I44132 DOI : / , IJREAM All Rights Reserved.

2 other methods. G. Aguilar-Torres et al. [6] proposed figerprit recogitio by usig local feature ad Humomet for verificatio purpose. The combiatio of FFT ad Gabor filter was used to ehacemet the figerprit images, ad they test their methods o FVC22 dataset ad the local feature used such as miutiae feature by usig Crossig umber (CN) to detect the type of miutiae feature. They solve the problem of rotatio ad traslatio movemet which hadled by scaer ad they obtaied high recogitio rate with =.8 % with accuracy =95.3%. Leo-Garcia et al.,[7] work o figerprit recogitio based o ivariat momets which tested o 5 images for both cases good ad poor quality ad they used FFT ad Gabor filter to ehace the clarity of figerprit images. They used crossig umber to extract the miutiae feature. They resulted that FFT=76.85% ad Gabor=8.55% while by usig both of them the accuracy reached to 85.75%. Che ad Li[8] proposed comparative study of aalysis ad combiig differet types of features by usig differet fusio schemes like Neyma Pearso rule ad SVM. The feature was used such as Miutiae, Miutia Descriptor, Ridge Feature Map, Orietatio ad Ridge Desity Map. They said that results improve the recogitio performace by apply differet combiatio. Qiogxiu Li et al.,[9] proposed the multi-feature of figerprit with score fusio ad got 97.5% accuracy. Park ad Park [1] preseted a ew approach for figerprit classificatio based o Discrete Fourier Trasform (DFT) ad oliear Discrimiat Aalysis. By utilizig DFT ad directioal filters, a reliable ad efficiet directioal image is costructed from each figerprit image, ad the oliear discrimiat aalysis is applied to the costructed directioal images, reducig the dimesio dramatically ad extractig the discrimiat features. Experimetal results demostrate competitive performace compared with other published results. III. METHODOLOGY The Methodology of the system coverig differet steps start from collect the data from the dataset ad preform the steps to remove the oise ad make the images clear for extract the features by usig LBP feature extractio method which related to extract the texture feature from figerprit fially the feature was stored i dataset as template for matchig stage which preform the matchig betwee the test images ad template i database ad store the similarity score betwee them i matrix which called matchig score. Figure 1 shows the block diagram of the figerprit idetificatio system ad the ext sectio give the explaatios of each step i brief. Figure 1 Methodology of the system A. Acquisitio of Figerprit The acquisitio i this work was coducted by collected dataset from differet sources which stadard available olie ad other take by requested the ower of that datasets. I the case of figerprit there are four datasets amely figerprit verificatio competitios FVC2, FVC22, FVC24 ad KVK datasets with same size of 1 subjects with eight impressios. Table 1 shows the characterizatios of each dataset [11]. Dataset FVC 2 FVC 22 FVC 24 Table 1 Figerprit datasets characterizatio Type Subject/ sample Image size Resolutio Sesor DB1 _B 11 x8 3x3 5 dpi Optical DB1 _B 11 x8 388x374 5 dpi Optical DB1 _A 11 x8 64x48 5 dpi Optical KVK /8 48x48 5 dpi L sca 5P Ad the fig.2 shows some sample take from each datasets (a) (b) (c) (d) Figure 2 Figerprit samples from (a) FVC2 (b) FVC22 (c) FVC24 (d)kvk B. Figerprit Pre-processig To extract the proper feature from ay figerprit images, these images have to be uder clarity ad quality measures, this measure ca be achieved uder the pre-processig stage which leads to remove the oise ad uwated data by usig ehacemet techiques. I this work, the double ehacemet techiques were used to give the figerprit images more clarity. I the begiig, the histogram equalizatio was used the the False Fourier Trasformatio (FFT) was applied which was derived from [12]. The image was divided to overlappig blocks ad gradiet was computed for each block to determie ridge orietatio of a image ad obtaied the FFT values, afterward the smoothig was used to geerate a coherece images, fially the regio mask was geerated by threshold the images. For each overlappig block the agular ad radial filter were geerated o orietatio ad frequecy images respectively. I additio, the ridge orietatio filed estimatio [13, 14], ridge frequecy[15,16] ad mask iformatio were applied for each blocks. Afterward, the Fourier domai cotextual filterig was applied for filterig all blocks ad the Biarizatio is coducted by locally adaptive threshold algorithm which derived from [17, 18]. The details of pre-processig were discussed i our previous work [19, 2]. The image ehacemet was recostructed by filter the block i FFT ad composig each block ad fially the regio mask was applied to figerprit image for ehacemet. Figure 3 shows the preprocessig with first ad secod ehacemets. The details 396 IJREAMV4I44132 DOI : / , IJREAM All Rights Reserved.

3 of figerprit processes were discussed i [21, 22]. After applied the followig steps like Idetify ridge segmet, Determie ridge orietatios, Determie ridge frequecy, Apply filters, Histogram Equalizatio ad FFT Ehacemet [12]. (a) (b) (c) Figure 3 Pre-processig stages (a) origial image (b) Level 1 ehacemet(c) Level 2 ehacemets The secod step of pre-processig is Biarizatio of figerprit image which is a process to trasform the image from 256 levels to two levels(,1)where the correspodig to black, ad 1 correspodig to white, The result of Biarizatio show i Fig.4 i this paper we used locally Adaptive Biarizatio method which is summarized i this steps below : The image divided ito blocks with size 16x16. Calculate the mea itesity value for each block. For each pixel is apply this rule 1 if itesity value > mea itesity value for curret block Pixel if itesity value < mea itesity value for curret block C. Feature Extractio The goal of feature extractio is to extract the (global or local) iformatio which represets the image i differet domai. The texture feature usig Local Biary Patter (LBP) was used for figerprit idetificatio system. The process of LBP was discussed i ext sectio i brief. The Local Biary Patter (LBP) first used by (Ojala et al.,[25,26] as texture feature to extract the iformatio from whole image ad it is ot required to extract the Regio of iterest (ROI) of a image[27] because LBP work o itesity value of each pixel. That meas it refers to biary code of a image ad represets the iformatio related to each eighborhood from that s pixel. The LBP feature ca be determied by take the ceter pixel as threshold value which compared with all the eighborhoods to geerate the biary code ad covert to decimal by Eq.(1). The example of LBP calculatio is show i Fig.6 ad for more details foud i [28,29]. 7 c c c c L B P( x, y ) 2 I I x, y Where I ad Ix, y c c are the values of eighbor pixel ad ceter pixel respectively, = idex of eighbor. 1, if x sx, if x (1) (2) Figure 4 The secod step of pre-processig (a) Ehacemet image (b) Biarizatio The third step of pre-processig is thiig which shows i Fig.5 it is also called (skeletoizatio). To ehace the biary image the thiig algorithm is used to reduce the ridges of figerprit images. There are umber of thiig methods. The most popular thiig algorithms are medial axis method, cotour geeratio method, local thickess based thiig approach, sequetial ad parallel thiig [23, 24]. We used morphological operatio o biary image, the mai steps to do thiig is: Clea up the thi image by Remove sigle isolated, Removes H-Breaks ad Removes spikes. Remove the coected regio at the boudary. Figure 5 Example of figerprit image thiig Figure 6 Stages of LBP calculatio process. Algorithm 1 describes the LBP step by step. Algorithm 3.3: Feature Extractio LBP 1: Iput : Figerprit images after preprocessig 2: Output: LBP Features. 3: Begi 4: For each images do 5: Divided figerprit images to(8x8) overlap blocks 6: For each pixel i the blocks do 7: Compare pixel to all 8 eighbors 8: IF Ceter pixel > eighbors the 9: Replace the eighbors to 1 1: Else 11: Replace the eighbors to 12: Ed 13: Geerate the biary code from all the eighbors ad covert it to decimal 14: Apply histogram for all the cell (i which their eighbors grater of smaller to ceter pixel) FV LBP ( ROI ) 15: LBP hist () i N i1 16: Store the as feature vector 17: Ed 18: Ed 397 IJREAMV4I44132 DOI : / , IJREAM All Rights Reserved.

4 D. Matchig I this sectio, the features were received from feature extractio stage ad the matchig start by comparig the feature vector from iput (query image) with feature vector of template which was take at erollmet stage, ad the matchig score was geerated for each subjects. The matchig scores either similarity score or distace betwee two feature vectors. Afterward, the score stored as matrix for decisio purpose. I this work the Euclidea distace was used to perform the matchig betwee query image ad template. Suppose there are two vectors oe for figerprit FV for which defie as Hece, the Euclidea distace ca be defied as Eq.(3): d( FV ) ( FV FV ) i, j1 i Where is the umber of features poits i feature vectors (FV). Algorithm 2 describes the Matchig step by step. Algorithm 3.7: Matchig 1: Iput : Figerprit feature vector 2: Output: Score matrix, Threshold values. 3: Begi : 4: For each test feature of each Subject do 5: Test_FVi 6: For each template feature of each Subject do 7: Template_FVj dist( Test, ) ( ) 2 i Temp j Test FVi Template FV j i& j1 8: Score_matrix= d (Test i, Temp j ) 9: Ed 1: T = Score_matrix; /* total threshold values of system 11: mita = mi(mi(t )); /* miimum score 12: maxta = max(max(t )); /* maximum score 13: = 1; /* size of optimal threshold values 14: = (maxta - mita) / ; 15: cost = 1:1: ; /* threshold vector 16: Ed 17: Store score_matrix i database as math file for discussio purposed. 18: Ed E. Decisio I this stage, fial decisio for idetify the perso by his /her biometrics data was take either idetify or oidetify (Accepted or Rejected). The geuie score ad impostor score ca be separated from the score matrix with the help of threshold values (T ) which were geerated for each subject of the score matrix at the matchig stage. The decisio ca be determied by Eq.(4) j 2 (3) Accepted, if Score T Decisio Rejected, if score T IV. RESULTS AND DISCUSSION This part of the research work was covered the results of figerprit idetificatio based o LBP method which tested o four datasets amely: FVC2, FVC22, FVC24 ad KVK datasets with size of 1 subjects each have 8 samples. The experimetal performace was evaluated by usig differet parameter like False Acceptace Rate, False Rejectio Rate ad Equal Error Rate o differet threshold values. The cofiguratio of the system was implemeted o the laptop Dell, with Itel core i3 ad CPU 2.2 GHz with RAM 8.GB, widows 7 ad MATLAB with image processig tools versio 213a. The Eq.(5),(6) ad(7) shows the evaluatios matrix calculatio Impostor Score exceedig thershold 1 All Impostor Score Geuie Scores fallig below thershold 1 All Geuie Scores EER 2 I additio to the above parameters there is Geuie Accept Rate (GAR) Eq.(8). GAR 1 A. Results of LBP Feature o all datasets The evaluatio of LBP feature extractio techique o figerprit modal was calculated for all samples i datasets ad the feature vector was created ad score matrix was geerated. From the score matrix the geuie ad impostor score was extracted ad the evaluatio process was doe with help of evaluatio parameter above. After the evaluatio the results obtaied from FVC2 gave the of % ad of 6.25% with the lowest EER of % with the highest GAR of % at the threshold values (T ) of.2. Similarly, the FVC22 dataset was achieved of ad of 26% with EER of % ad GAR of % at the threshold values (T ) of.11. I additio, the results of FVC24 dataset were achieved of 9.25%, of % ad EER of % with GAR of 9.74% at the threshold values (T ) of.2. The fial result of KVK dataset was achieved of %, of 26% ad EER of % with GAR of % at the threshold values (T ) of.2. From all the results we coclude that the FVC2 gave miimum EER with highest GAR as compare with other datasets. Tables(2-5) shows the results of LBP for all the datasets ad figures(7-1) represet the ROC curve of the relatio betwee ad of the system for all the datasets while the figure 11(a) represet the ROC curve of EER for all datasets. Figure 11(b) depict the ROC curve of performace of the system by plottig GAR agaist the at differet T values. Table 2 Results of LBP feature based o FVC2 dataset FVC2 T EER GAR (4) (5) (6) (7) (8) 398 IJREAMV4I44132 DOI : / , IJREAM All Rights Reserved.

5 (%) FVC2 T EER GAR Matchig Threshold Figure 7 Relatio of ad of LBP o FVC2 Table 3 Results of LBP feature based o FVC24 dataset FVC24 T EER GAR Matchig Threshold Figure 8 Relatio of ad of LBP o FVC24 Table 4 Results of LBP feature based o FVC22 dataset FVC22 T EER GAR FVC22 T EER GAR Matchig Threshold Figure 9 Relatio of ad of LBP o FVC22 Table 5 Results of LBP feature based o KVK dataset KVK T EER GAR Matchig Threshold Figure 1 Relatio of ad of LBP o KVK (%) (a) EER Lie FVC2 FVC22 FVC24 KVK 399 IJREAMV4I44132 DOI : / , IJREAM All Rights Reserved.

6 GAR (1-) (%) FVC2 FVC22 FVC24 KVK (%) (b) Figure 11 Performace of LBP feature o all datasets (a) ROC of EER curve (b) GAR curve V. CONCLUSION AND FUTURE SCOPE This research work was ivestigated the texture feature by usig LBP techique for figerprit idetificatio. The work was coducted ad evaluated o four datasets as comparative study. There were four experimets of FVC2, FVC22, FVC24 ad KVK dataset. the results of LBP for all the datasets shows that FVC2 dataset gave the best results as compare with other datasets with miimum EER of % ad highest GAR of 96.15% while the KVK dataset give lower results compare with other dataset with highest EER of 22.11% ad lower GAR of 77.99% the overall coclusio the LBP texture feature give the efficiecy results with stadard dataset with higher results ad less EER. The work may be extedig to implemet the figerprit idetificatio based o fuzz ad eural etwork to improve the performace of the idetificatio system. REFERENCES [1] R. M. Bolle, J.H. Coell, S. 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