In this paper, we propose an new enhancement of the classification for damaged fingerprint database.It is based on the fact that damaged fingerprint image is composed of regular texture regions that can be successfully represents by co-occurrence matrices.So, we first extract the features based on certain characteristics and then we use these features to train a neural network for classifying fingerprints into five classes.The obtained results compared with existing approaches demonstrate the superior performance of our new

enhancement

Gunasekaran, ,

Josphineleela, R.

Ramakrishnan, M.

UUM Repository

A New Enhancement Of Fingerprint Classification For The Damaged Fingerprint With Adaptive FeaturesR.Josphineleelaa, M.Ramakrishnanb And GunasekarancaDepartment of information technology, Panimalar engineering College, Chennai, ilanleela@yahoo.com, Research scholar in Sathyabama University, Chennai, b Department Of Information Technology, Velammal Engineering College, Chennai, c Department Of Information Technology, Panimalar Engineering College, ChennaiABSTRACTIn  this  paper,  we  propose  an  new  enhancement  of  the  classification for damaged fingerprint database. It is based on the  fact  that  damaged  fingerprint  image  is  composed  of  regular texture regions that can be successfully represents by  co-occurrence  matrices.  So,  we  first  extract  the  features  based  on  certain  characteristics  and  then  we  use  these  features to train a neural network for classifying fingerprints  into five classes. The obtained results compared with existing  approaches demonstrate the superior performance of our new enhancement.KeywordsFingerprint classification, damaged fingerprint,Texture representation, enhancement, and neural network1.0 INTRODUCTION Damaged  Fingerprint  classification,  which  classifies fingerprints into a number of predefined categories, is useful as  the  preliminary  step  of  matching  process  because  it reduces  the time taken in fingerprint  identification.  In  this system ,which categorizes a damaged fingerprint into one of the  five  classes  such  as  whorl(W),right  loop(R),Left loop(L),arch(A) ,tented arch(T) by using their global features has  been  widely  used  for  damaged  finger  classification. There  are  many  different  ways  to  extract  and  represent global features which can be largely divided into three main categories:  singularity-based  approach,  structure-based approach  and  frequency-based  approach,  In  the  LOOP pattern are two focal  points:  the Core  or  the center  of  the loop, and the delta. The Delta is the area of the pattern where there  is  a  triangulation  or  a  dividing  of  the  ridges.  When recording  fingerprints,  the  delta  and  the  area  between  the delta  and  the  core  must  be completely recorded  .A Whorl pattern will have two or more deltas. For a whorl pattern, all deltas  and  the  areas  between  them must  be  recorded.  The Arch pattern has no deltas or core, but it too, must be fully recorded so that its individual characteristics can be readily distinguishedFigure 1: Deformed fingerprints and fingerprint classification schema involving six categories (a) arch (b) tented arch (c) right loop (d) left loop (e) whorl and (f) twin loop. Critical points in a deformed fingerprint called core and delta are marked as squares and triangles. Note that an arch does not have a delta or core one of the two deltas in (e) and both the deltas in (f) are not imaged. A sample minutiae ridge ending and ridge bifurcation(x)is illustrated in(e)Each image is 512*512 with 256 gray levels and is scanned at 512 dp resolution. All features points were manually extracted by one of the authors.2.0 PREVIOUS WORKMany approaches to automatic fingerprint classification have been presented in the literature and the research on this topic is still very active. The approaches are mostly based on two main  features  in  a  damaged  finger.1)  Global  ridge  and furrow  structures  that  form  special  patterns  in  the  central region  of  the  damaged  finge.2)  Local  ridge  and  furrow minute details. A damaged finger is classified based on the second type of features. There are two main approaches for extracting information about  fingerprint  ridge structures.  One method is based on developing a mathematical  model of  fingerprint  ridges and representing  the  fingerprint  using  these  models.  Another approach uses record characteristics of the ridges and stores this information for classification.        Recently, some papers have reported very good results in the automatic classification of fingerprint databases.Figure 2: The sequence images of  adaptive feature extraction (a) original  image  (b)  orientation  field(c)  variation  field  (d)  feature region (e) directional value of the feature region Jain  used  the  Gabor  filter  in  four  directions  to  extract features  form  fingerprints  for  classification  .In  another attempt;  they  have  stored  the  shape  information  about  the structure of fingerprint ridges for their classification scheme. Each ridge is classified as nonrecurring,  recurring,  or fully recurring. They obtained a 94.8% correct classification. Jain and L.Hang have proposed a mathematical  model  for each fingerprint  class  that  represents  the  ridge  structure  of fingerprints belonging to that class. They obtained an 91.3% correct  classification  .chang  and  Fan  have  developed  an alternate  fingerprint  representations  that  captures  structural information. Yaoetal have developed an algorithm based on support  vector  machines.  They  obtained  a  94.7%  correct classification.      We propose enhancement method for the damaged finger classification  based  on  the  fact  that  fingerprint  image  is composed of regular texture regions that can be successfully represents  by  co-occurrence  matrices.  We  first  apply histogram  equalization  for  reducing  the  influence  of  the noise  in  damaged  fingerprint  images.  Then,  a  series  of simple  morphological  operations  will  be  applied  to emphasize  the  ridge  structure  of  damaged  fingerprint patterns.The texture context  of fingerprint  structure will be represented  by  co-occurrence  matrices  that  specify  the relation  of  neighbor  pixels  in  certain  distance  in  a  given direction.  Based  on  the  applied  co-occurrence  matrices, some features  are  defined  and extracted.  Finally,  based on neural  network  the  system  will be trained and used for classify  the  damaged  fingerprint  databases  on  four major classes.3.0 HISTOGRAM EQUALIZATION Because there are many noises in original fingerprint image, an  image  enhancement  algorithm  such  as  histogram equalization is usually applied to reduce the influence of the noise  in  fingerprint  image  and  to  emphasize  the  ridges structures of damaged fingerprint patterns. Figure4: damaged fingerprint classification experiment samples4.0 NORMALIZATIONThis  part  of  algorithm  has  significant  effect  on  total performance  of  algorithm.  The  problem  is  that  the  total number  of  compared  pixels  pairs  is  different  due  to  the angular  relationships.  Moreover,  the  size  of  images  in the databases is not the same. To overcome these problems, it is necessary to normalize the co-occurrence matrices. We used the normalized correlation algorithm proposed by kim et al to normalize all co-occurrence matrices.5.0 FEATURE EXTRACTIONIn the feature extraction phase, the feature region is divided into 8*8 blocks (fig 2 (d))  and their directional  values are estimated [fig 2(e)].here the directional field θ is calculated similarly to the orientation field computation except the size of each block that is different for each fingerprint due to the adaptively  selected  feature  region.  The  proposed  method extracts  the  256  dimensional  feature  by  estimating  the difference between the directional value θ(i,j) of the (i,j) the feature-block  and  each  of  four  directions,0o,45o,90o  and 135o as follows                     kf       =    ji ,∂−  if  ji ,∂ <0                                            −pi ji,∂ 2pi≥                        (1)                                           ji ,∂ OtherwiseWhere ji ,∂  = ),( jiθ  –γ, γ = {0, π⁄4, π⁄2, 3π⁄4  }       (2) Figure 3: Architecture of an automatic identity authentication system6.0 CLASSIFIERFor the classification task,  an artificial feed-forward neural network with two hidden layers is used. This neural network has Table 1: Five classification results on the NIST-4 databaseA-Arch-Tented Arch-Left Loop-Right Loop-Whorl60 input neurons (12 features multiplied by 5 co occurrence matrices) and 5 output neurons corresponding to 5 classes of arch,  whorl,  right  loop,  left  loop,  and  tented  arch.  The number  of  neurons  in  hidden  layers  is  determined experimentally.The classification algorithm summarized here (see Figure ii) essentially  devices  a sequence  of  tests  for  determining  the class of a fingerprint and conducts simpler tests earlier in the decision  tree.  For  instance,  two  core  points  are  typically detected  for  a  whorl  (see  figure  11)  which  is  an  easier condition  to  verify  than  detecting  the  number  of  type-2 recurring ridges. Another highlight of the algorithm is that if does  not  detect  the  salient  characteristics  of  any  category from  features  detected  in  a  fingerprint,  it  recomputed  the features with a different pre processing method. For instance, in the current implementation, the differential pre-processing consists of different  method/scale of smoothing. As can be observed from the flowchart that algorithm detects (i) whorls basedupon  detection  of  either  two  core  points  or  a  sufficient number of  type-2 recurring  ridges;  (ii)arch based upon the inability  to  detect  either  delta  or  core  points; (iii)left(right)loops  based  on  the  characteristics  tilt  of  the symmetric  axis,  detection  of  core  point  and  detection  of either a delta point or sufficient number of type-1 recurring curves;  and(iv)  tented  arch  based  on  relatively  upright symmetric axis, detection of core point and detection a delta point or sufficient number of Type-1 recurring curves Figure 5: Flowchart of damaged fingerprint classification .The re-compute involves starting the classification algorithm wit different preprocessing (e.g. smoothing of the image)Table  1shows  the  results  of  the  fingerprint  classification algorithm  on  the  NIST-4  database  which  contains  4,000 images (image size is 512*480)  taken from 2000 different fingers,2  images  per  finger.  Five  fingerprint  classes  are defined:1)Arch ,ii)Tented arch,  (iii)left loop,(iv)Right Loop and (v)  Whorl  .Fingerprints  in this database  are  uniformly distributed among these five classes (800 per class).The five class  error  rate  in  classifying  theses  4,000  fingerprints  is 12.5% the confusion matrix is given table 1;numbers shown in  bold  font  are  correct  classifications  for  damaged fingerprint.  Since  a  number  of  fingerprints  in  the  NIST-4 database are labeled as belonging to possibly two difference True Class Assigned classA T L R WA 88 13 10 11 0T 17 384 54 10105L 31 27 75 3 25R 30 47 3 70 15W 6 1 25 10 75classes, each row of the confusion matrix in Table 1 does not sum  up  to  800.For  the  five  class  problem,  most  of  the classification errors are due to misclassifying a tented arch as an arch. By combining these two arch categories into single class,  the  error  ratedropsfrom12.5%  to  7.7%  besides  the tented arch-arch errors,  the other  errors  mainly come from misclassifications  between  arch/tented  arch  and  loops  and due to poor image quality7.0 EXPERIMENTSTo  test  proposed  system,  two  databases  were  used.  The famous  databases  are  containing  FVC2000,  FVC2002,  and FVC2004 from biometric  system Laboratory  University  of Bologna  and  other  from  Neurotechnologija  web  site nameVeriFinger_sample_DB.NIST4  fingerprint  database, which consists of 4000 images (2,000 fingerprint pairs) for our experiments, the first 1,000 pairs of fingerprints (F0001 to F1000 and S0001 to s1000) were used for training and the reaming 1,000 pairs (F1001 to F2000 and S1001 to S2000) were used for testing.Figure 6: Five classes of damaged fingerprint  A. Adaptive features versus non adaptive featuresTwo types of fingerprint features were used for comparison experiments:  adaptive  and  non  adaptive.  The  adaptive features  were  extracted  by  using  the  proposed  algorithm, while the non adaptive features were obtained by applying feature regions of fixed size (128*128,192*192,256*256 and 320 *320 pixels)Figure 7: Examples of the selected feature region for two(upper and lower)different fingerprints(a)Results of the adaptive approach, and others are results of the non adaptive approaches where the sizes of feature regions are fixed as:(b)128*128,(c)192*192,(d)256*256 and (e)320*320 pixels.8.0 CONCLUSIONWe  have  proposed  a  new  enhancement  of  fingerprint classification  based  on  the  structure  approach  to  damaged fingerprint  .The  features  extracted  from  the  matrices  can well characterize the regular texture of damaged fingerprint images. We obtained the results 94.8%. REFERENCES  1.  R.  Cappelli,  A.  Lumini,  D.  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A new enhancement of fingerprint classification for the damaged fingerprint with adaptive features

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A new enhancement of fingerprint classification for the damaged fingerprint with adaptive features

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