Fingerprint Direct

8/16/2019 Fingerprint Direct

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On Analyzing of Fingerprint Direct-Access Strategies

G. Indrawan1, S. Akbar

2, B. Sitohang

3

123Data & Software Engineering Research Division - School of Electrical Engineering and Informatics

Bandung Institute of TechnologyBandung, Indonesia

[email protected],

[email protected],

3 [email protected]

Abstract — This paper analyzed several aspect of publicly

available fingerprint direct-access strategies over three public

data sets, including their advantages and drawbacks, related

works, and accuracy performance comparison on the graph of

error rate and penetration rate. On recent research, smaller

penetration rate at certain error rate (smaller area under curve)

could be obtained by certain strategy. Penetration rate equal to

5% could be produced at error rate equal to 4%, 10%, and 1%

on FVC2000 DB2A, FVC2000 DB3A, and FVC2002 DB1A,

respectively. Through this analysis, hopely it could be used as afoundation for better future developments over new strategies or

future improvements over existing strategies in area of

fingerprint identification, especially to obtain more accurate

result with smaller area under curve and achieved with faster

search time.

Keywords— direct-access; error rate; fingerprint; penetration

rate

I. I NTRODUCTION

Recent performance comparison in area of fingerprintdirect-access strategy [1], leaving further question on how farits accuracy and efficiency performance can be improved. In

general, direct-access strategy itself means any searchingstrategy to output a candidate list (CL) without performingone-to-one matching between a query and candidates in thedatabase. A CL from a query will have a list of certain ErrorRate (ER) at certain Penetration Rate (PR). The ER is theaverage percentage of searched queries that are not found, andthe PR is the portion of the database to be searched on theaverage. The accuracy performance is then measured by thegraph of the trade-off between ER and PR that shows at certainER how low PR can be achieved. The efficiency performancewas indicated by its search speed, memory usage andscalability (ability to search on larger database withoutaccuracy degradation).

Based on above question, analysis was conducted onseveral publicly available fingerprint direct-access strategiesover three public data sets, with motivation that this analysiscould be used as a foundation for better future developmentsover new strategies or future improvements over existingstrategies. An analysis was focused on accuracy performancesince systematic comparison can be conducted independentlyfrom hardware platform.

Several proposed fingerprint direct-access strategies have been roughly classified by Cappeli [1], i.e. 1) using globalfeatures such as global ridge-line frequency (e.g., [2]); 2) local

features such as local ridge-line frequency (e.g., [1], [3]), localridge-line orientations (e.g., [1], [2], [3], [4], [5], [6]), and localfeatures from the orientation image (e.g., [7], [8]); 3) minutiaefeatures such as geometric features from minutiae points and

perform searching through hashing strategy: triplets (e.g., [9],[10], [11], [4]), and local-star (e.g., [12], [13]); 4) other featuressuch as FingerCode (e.g., [4]), ridge curvature (e.g., [14]), andSIFT features (e.g., [15]), and matching scores to constructindex keys (e.g., [16], [17]). So, it is quite challenging to docomparison analysis for these various strategies on this area.

The rest of this paper was organized as follows. Section 2compares pros and cons of several direct-access strategiesabove. Section 3 analyses experiments on accuracy result overthree public data sets (several strategies above have no resultsusing those data sets). Finally, Section 4 draws the conclusion.

II. DIRECT ACCESS STRATEGIES

Table I shows three publicly available data sets and their

image samples were shown by Fig. 1. Pros and cons of several

direct-access strategies, including aspect of novelty, features

extraction, indexing and retrieval mechanism were described

hereafter.

TABLE I. THREE PUBLIC DATA SETS FOR ACCURACY COMPARISON.

No Data Set Sensor Type Pixels Direct Access Strategies

1 FVC2000

DB2A[18]

Low-Cost

CapacitiveSensor

256 x

364

De Boer at al. (2001) [4]

Jiang et al. (2006) [2]Liang et al. (2006) [11]

Capelli (2011) [1]

Indrawan et al. (2014) [13]

2 FVC2000DB3A

[18]

OpticalSensor

448 x478

Jiang et al. (2006) [2]Capelli (2011) [1]

Indrawan et al. (2014) [13]

3 FVC2002DB1A

[19]

OpticalSensor

388 x374

Liang et al. (2007) [20]Shuai et al. (2008) [15]

Capelli (2011) [1]Indrawan et al. (2014) [13]

Figure 1. From left to right: sample

fingerprints from FVC2000 DB2A,

FVC2000 DB3A, and FVC2002

DB1. For most of strategies, first

impressions were used for indexcreation (top row), and the others

(bottom row) were used for queries.

Noted, images are not in their actualsize but they are in their scale

difference.



A. De Boer et al. (2001) [4]

The advantages of the proposed strategy start from itsnovelty

1 on indexing by combining multiple features of

Directional Field (DF), FingerCode, and minutiae triplets. Thehighest rank combination of multiple features results in aconsiderably better performance than the schemes that are

based on the individual features. High-resolution segmentationmethods by Bazen et al. (2001) [21] was used that possibly can

eliminate a DF that is estimated from a bad-quality fingerprintimage. An alternative registration point also was defined for anarch type fingerprint that does not contain any singular pointsfor DF estimation.

The drawbacks of this strategy start from its dependency onsingular point for registration points used in DF andFingerCode algorithm, with no anticipation for wrong or non-existing detection (on experiment, need manual correction forwrong detection or manual rejection for non-existingdetection). Running on a 450 MHz Intel Pentium II CPU,FingerCode extraction time takes about 40 seconds, longer thanDF feature vector extraction time, which takes about 15seconds. Searching 2.5% of database, only 77% of queries were

found by the minutiae triplets algorithm which was muchworse than the results by Bhanu et al. (2001) [22] where 100%of queries is found in the top 10 of a database of 400 fingers.This discrepancy might be because of some hiddenimplementation aspects by Bhanu et al. (2001) [22]. Addingminutiae triplets to any combination of features does notimprove the performance significantly. This could be explained

by the previous cause. Moreover, this strategy utilized nomultithreaded algorithm for faster implementation which wasindicated by CPU type mentioned previously.

Related works to unveil sub methods used by this strategywere based on minutiae-triplets based indexing [9], [22]; DF asthe local orientation of the ridge-valley structures [23], [24];

singular points as the discontinuities in the DF [24], [25];segmentation methods possibly to eliminate a DF from a bad-quality image [21]; and FingerCode extraction [26], [27].

B. Jiang et al. (2006) [2]

The advantages of the proposed strategy start from itsnovelty on indexing by combining multiple features oforientation field (main retrieval feature) and dominant ridgedistance (auxiliary retrieval feature). No minutiae needed forindexing since it was extracted through relatively long processthat could bring out spurious features and/or eliminate genuinefeatures. The dominant ridge distance is more robust to thenoise than the simple average ridge distance of a fingerprint.

Also, a new distance measure better quantifies the similarityevaluation between two orientation fields than the conventionalEuclidean and Manhattan measures. Moreover, a variablesearch tolerance was introduced for more efficient retrieval. Database clustering mechanism was also introduced with a fewclusters comparation on retrieval. Experiments show that theclustering-based approach of this strategy achieves betterretrieval results than various exclusive classification methods.

1 Authors believe that novelty is part of the advantage for knowledge

development.

The drawbacks of this strategy start from its accuracy androbustness that was depended on the reference point (itslocation and its direction) to produce a feature vector invariantto the translation and rotation. In fact, a substantial portion ofthe retrieval errors is caused by the falsely or inconsistentlydetected reference point due to poor fingerprint quality (seenext Section III Point 7). Moreover, no notice of multithreadedalgorithm utilization by this strategy.

Related works to unveil sub methods used by this strategywere based on the gradient-based estimation method for theOrientation Field (OF) computation [28], [29], [24];measurement of an anisotropy of OF estimation [30], [31]; thethree features based segmentation algorithm: block mean grayvalue, variance of gray values, and coherence [21]; and two-stage estimation method for the OF computation [32].

C. Liang et al. (2006) [11]

The advantages of the proposed strategy start from itsnovelty on indexing by combining multiple features ofminutiae detail and Delaunay triangle (its handedness, angles,maximum edge, and related angle between orientation field and

edges). Minutia detail represents not only the type but also theshape, which provides more minutiae classes than minutiatypes, so it will reduces search space. Delaunay triangle, whichdescribes of local similarity, provides more insensitivity toelastic distortion. Since Delaunay triangulation creates O(n)triangles, which is much less than O(n

3) triangles created by

triplets based algorithm, more redundant or wrong matchedtriangles are avoided. It will reduce search space using a sameT M as a threshold of maximum number of triangles. Thisstrategy utilized existing minutia extraction algorithm withrelatively small additional computing effort to obtain minutiadetail. All of the correspondences found in indexing can workas control points for estimating the nonlinear mapping function

between two fingerprints during distortion compensation. Moreover, this strategy does not depend on singular point foralignment, avoiding wrong or non-existing detection.

The drawbacks of this strategy start from its minutia detail,which is based on bifurcation-type minutia, would provide lessdiscriminator information on fingerprint whose lack number ofthis minutia type. This type of fingerprint would come from

partial or low-quality image where small number or wrong bifurcation-type minutiae were detected. Also, no notice ofmultithreaded algorithm utilization by this strategy. Moreover,through massive experiments, Liang et al. (2007) [20] showsthat Delaunay triangulation may not be stable if even a tinydistortion is applied on prints.

Related works to unveil sub methods used by this strategywere based on minutiae-triplets based indexing [9], [22];improved minutiae triplets by orientation field and FingerCode[4]; and improved minutiae triplets by novel features e.g.,triangle angles, handedness, type, and direction [10].

D. Liang et al. (2007) [20]

The advantages of the proposed strategy start from itsnovelty by using Low-order Delaunay triangle (LoD triangle)on improvement of indexing of combined multiple featuresabove by Liang et al. (2006) [11]. This strategy includes all of



the advantages by Liang et al. (2006) [11], with the additionalone on insensitivity to small shifts of minutiae by distortionthat reduce time and space complexities (direct effect) andnarrows down the search space in database (indirect effect) bycreating only O(n) Delaunay triangles.

The drawbacks of this strategy are the same as thedrawbacks belong to Liang et al. (2006) [11], with theadditional one on more computational cost to generate certainnumber of additional order 1-Delaunay triangles, especially ondistorted fingerprints.

Related works to unveil sub methods used by this strategywere based on improved minutiae triplets by applyingDelaunay triangulation [33]; and improved minutiae triplets byusing minutia detail and Delaunay triangle [11].

E. Shuai et al. (2008) [15]

The advantages of the proposed strategy start from itsnovelty on indexing by using Scale Invariant FeatureTransformation (SIFT), which has been widely used in genericimage retrieval. Most of the minutiae points actually can also

be detected by SIFT interest point detector. Applying areducing procedure for SIFT results in slight loss ineffectiveness but great improvement in efficiency of retrieval. Moreover, by using Locality-Sensitive Hashing (LSH) on theindex construction phase, makes it could perform similarityqueries by only examining a small fraction of the database.

The drawbacks of this strategy start from its behaviorwithout a reducing procedure that will generate a large numberof features over a broad range of scales and locations. Also, theuncertainty of acquisition (e.g. partialness, distortion) washandled by using a composite set of features from multipleimpressions per finger, no handling if using single impression. Also, no notice of multithreaded algorithm utilization by this

strategy. Moreover, its accuracy comparison on FVC2000DB2A [18] to De Boer at al. (2001) [4] and Jiang et al. (2006)[2], was not head to head comparison because they used firstimpressions per finger for index creation, instead of randomfirst three impressions. Also, De Boer at al. (2001) [4] resultshave been obtained by using additional 80 fingerprints oftraining set “B” [18] (more detail, see next Table II).

Related works to unveil sub methods used by this strategywere based on LSH to index the set of 128-d SIFT descriptors

[34], [35]; and SIFT [36], [37].

F. Capelli (2011) [1]

The advantages of the proposed strategy start from its

novelty on indexing based on vector and scalar features,obtained from ridge-line orientations and frequencies. Itdepend on singular point (core) for alignment using twodetection approach [38], [39] where in case of ambiguities,

both approaches are allowed to propose up to five candidates. Running on a 2.66-GHz Intel Core 2 Quad CPU, scalabilityexperiments on a data set containing one million syntheticfingerprints achieved very good results: indexing one millionfingerprints requires 709 MB of memory, and a search oversuch a large data set takes less than 1 s. Moreover, multithreaded algorithm was already implemented.

The drawbacks of this strategy start from its limitation onlow fingerprint quality that causing incorrect core detectionand/or very noisy features to be extracted. Also, its limitation ison core point that was not present in the image and/or smalloverlapping region between the two fingerprints. Anotherlimitation is on large rotation because the proposed featuresand score measures cannot tolerate a large amount of rotation

between the query and candidate in the database. On FVC2002

DB1A [19] which have largest number of good quality images based on NFIQ measurement [13], this strategy result gaverelatively higher ER at PR below 10% than result on FVC2000DB2A [18]. It need work out to solve this symptom.

Related works to unveil sub methods used by this strategywere based on the traditional gradient-based technique toestimate the local orientations [40]; the fingerprint patternsegmentation from the background. [28]; the local ridge-linefrequencies estimation based on the local orientations [29]; theiterative singularity approach for fingerprint core detection[38]; ridge-line normal approach for fingerprint core detection[39]; and similarity measure inspired by the distance measureof local orientation [2].

G. Indrawan et al. (2014) [13]

The advantages of the proposed strategy start from itsnovelty on indexing by using local-star structure, hashing-

based mechanism, multi stage similarity score computation,and variable-threshold-on-score-ratio based CL reduction. Itutilized existing minutia extraction algorithm that produceinformation of its Cartesian coordinate, orientation, and type,with relatively no additional computing effort. Also it reducedtime complexity for second stage verification [11][20] byreducing CL through variable threshold on score ratio. Thisstrategy do not depend on singular point for alignment,avoiding wrong or non-existing detection. Moreover,

multithreaded algorithm, was already implemented asdescribed by Indrawan et al. (2014) [12].

The drawbacks of this strategy start from unused extractedlocal orientations which has been found very useful forindexing in several other studies by Lumini et al. (1997) [41],Cappelli et al. (1999) [6], Cappelli et al. (2002) [5], Lee et al.(2005) [3], and Jiang et al. (2006) [2]. Also it still has noadvance database clustering mechanism for reducing searchspace. Moreover, no utilization of image quality algorithm bythis strategy. Running on a 2.40 GHz Intel Core 2 Quad CPU, aquery from FVC2000 DB2A (256 x 364 pixel) takes averagetime about 247ms, drastically increasing about triple time for aquery from FVC2000 DB3A (448 x 478 pixel) which takes

average time about 756ms. It need work out to suppress thissymptom.

Related works to unveil sub methods used by this strategywere based on hashing mechanism inspired by minutiae-triplets

based indexing [9]; improved local-star based matching fortranslation and rotation invariant [42]; minutia featuresextraction (Cartesian coordinate, orientation, and type) [43];variable threshold on score ratio to reduce CL length forverification [11], [20], [44]; and Hill-climbing learning foroptimization of 71 algorithm’s parameters [45].



III. EXPERIMENTAL R ESULT COMPARISON

Based on compilation by Capelli [1] and Indrawan et al.[13], Figs. 2 - 4 show accuracy of several fingerprint directaccess strategies on three publicly available data sets. Theexperiment design was summarized by Table II.

Figure 2. Accuracy of direct-access strategies on FVC2000 DB2A.



TABLE II. ACCURACY COMPARISON OF DIRECT-ACCESS STRATEGIES.

N

oData set Published strategies

Index from

each finger

1 FVC2000

DB2A [18]

(Fig. 2)

De Boer at al. (2001)2 [4]

Jiang et al. (2006) [2]

Liang et al. (2006)2 [11]Capelli (2011)3 [1]

Indrawan et al. (2014) (M3) [13]

Indrawan et al. (2014) (M) [13]

First imp.

First imp.

First imp.First imp.

First imp.

First imp.2 FVC2000

DB3A [18]

(Fig. 3)

Jiang et al. (2006) [2]Capelli (2011)3 [1]


Indrawan et al. (2014) (M) [13]


First imp.

First imp.

3 FVC2002

DB1A [19]

(Fig. 4)

Shuai et al. (2008) [15]

Capelli (2011)3 [1]

Indrawan et al. (2014) (M3) [13]Indrawan et al. (2014) (M) [13]

Liang et al. (2007)1, 2 [20]

Shuai et al. (2008)1 [15]Capelli (2011)1, 3 [1]

Indrawan et al. (2014) (M1, 3) [13]


Random imp.

First imp.


Random 3 imps.

Random 3 imps.First 3 imps.

First 3 imps.

First 3 imps.1 Results have been obtained by using three fingerprint impressions (3 imps.)

from each finger for index creation, instead of one (imp.).

2 Results have been obtained by using additional 80 fingerprints of training set“B” [18][19], resulting in 880 fingerprints from 110 fingers.

3 Results have been obtained by selecting Top 10% Scores of CL ( N T = N /10).

1. All of the results, except them with a superscript number

“1” (Table II), were obtained by using first fingerprint

impression from each finger for index creation, and the

remaining seven for queries (In this scheme, Indrawan et al.

[13] results were represented by black dashed-lines). The

results with a superscript number “1” were obtained by

using first three fingerprint impressions from each finger

for index creation and the remaining five for queries (In this

scheme, Indrawan et al. [13] results were represented by

grey dashed-lines). Shuai et al. [15] and Liang et al. [20]are the exceptions, as shown by Table II. They used randomimpression or random three impressions from each finger

for index creation. This will have a consequence on

unpredictable quality of created index since first impression

or first three impressions, as it represents or they represent

higher quality image(s) than others from the same finger,

was or were not selected for sure. Because of thatconsequence, random selection is not so appropriate for

head to head comparison. However, they are still worthy

shown to enrich analysis in this paper.


“2” (Table II), were obtained by using 800 fingerprints of

testing set “A” [18][19] from 100 fingers. The results with asuperscript number “2” were obtained by using additional

80 fingerprints of training set “B” [18][19], resulting in 880

fingerprints from 110 fingers. In another word, the results

with a superscript number “2” were obtained by using

additional 10 impressions for index creation and 70

impressions for queries. Both of the results cannot be

merged for head to head comparison. However once again,

the results with a superscript number “2” are still worthy

shown to enrich analysis in this paper.




“3” (Table II), were obtained without selecting Top 10%

Scores of CL ( N T = N /10), where N is number ofimpressions used by index creation (number of candidates

in database). The results with a superscript number “3”

were obtained by selecting Top 10% Scores of CL.

4. Based on previous points, Indrawan et al. [13] provides

results that come from two search mode: 1) up to matcher

stage with N T selection of CL (dashed-lines with trianglemark); and 2) up to matcher stage without N T selection of

CL (dashed-lines with round mark). For the next

discussion, two black dashed-lines with triangle and round

mark on Fig. 2, Fig. 3, and Fig. 4, each will be referred as

Matcher 3 (M

3) and Matcher (M). Whilst, two grey dashed-

lines with triangle and round mark on Fig. 4, each will bereferred as Matcher

1,3 (M

1,3) and Matcher

1 (M

1). As Liang

et al. [11], [20] stated that the fingerprint identification can

be divided into fingerprint indexing and fingerprint

verification, M3, M, M

1,3, and M

1 of Indrawan et al. [13]

could be considered as sort of fingerprint verification. As

shown by Table II, M3 and M

1,3 was provided for head to

head comparison each with Capelli3 [1] and Capelli1,3 [1],whilst M and M

1 was provided for head to head comparison

with other strategies. As confirmed by Fig. 2, Fig. 3, and

Fig. 4, M3/M

1,3 was less accurate than M/M

1. M

3/M

1,3 loss

its accuracy in certain degree compare to M/M1 because of

final CL selection by the second step of CL reduction [12]

which is simply selecting maximum top N T candidates ofCL if length of CL longer than N T .

5. This point refers to head to head comparison for strategies

utilizing N T . On Fig. 2 and Fig. 3, up to PR equal to 4%,

Indrawan et al. (M3) [13] gave lower ER rather than

Capelli3 [1]. Larger than PR equal to 4%, Capelli

3 [1] has

faster rate of decrease of its ER to PR, so it gave lower ER

rather than Indrawan et al. (M3) [13]. On Fig. 4, at all PR on

the graph, Indrawan et al. (M3) [13] gave lower ER rather

than Capelli3 [1], and Indrawan et al. (M

1,3) [13] gave lower

ER rather than Capelli1,3

[1]. Moreover, Indrawan et al.

(M1,3

) [13] gave step down ideal curve where it canmaintain its PR equal to 1% for smaller ER. It means for

every query, search result of top one of CL (1% of number

of fingerprint at index) always give correct candidate.

6. This point refers to head to head comparison for strategies

not utilizing N T . On Fig. 2, up to PR equal to 5%, Indrawan

et al. (M) [13] gave lower ER rather than all otherstrategies. Higher than PR equal to 5%, De Boer et al. [4]

(Combined) gave lower ER rather than Indrawan et al. (M)

[13]. Higher than PR equal to 13%, Liang et al. [11] gave

lower ER rather than Indrawan et al. (M) [13]. Higher than

PR equal to 22%, De Boer et al. [4] (Directional Field) gave

lower ER rather than Indrawan et al. (M) [13].

Unfortunately, Indrawan et al. (M) [13] cannot be

compared head to head to De Boer et al. [4] and Liang et al.

[11] because of different number of data set (see Table II).

Moreover, De Boer et al. obtained those results by

manually correcting the core point in 13% of thefingerprints and by discarding 1% of the fingerprints

because no core point could be found. Indrawan et al. (M)

[13] have been performed neither such manual adjustments

nor rejections. Furthermore, Indrawan et al. (M) [13] canonly be compared head to head to Jiang et al. [2] start at PR

equal to 5% where Indrawan et al. (M) [13] gave lower ER

rather than Jiang et al. [2]. On Fig. 3, up to PR equal to

22%, Indrawan et al. (M) [13] gave lower ER rather than

Jiang et al. [2]. Higher than PR equal to 22%, Jiang et al.

[2] gave lower ER rather than Indrawan et al. (M) [13]. OnFig. 4, at all PR on the graph, Indrawan et al. (M) [13] and

Indrawan et al. (M1) [13] gave lower ER rather than all

other strategies, even though no head to head comparison

exist because of different number of data set and different

impression(s) selection mechanism for index creation (see

Table II).7. Beside related to the used methods with their pros and cons,

accuracy performance comparison results also related to the

used data set characteristics. Several points about these

characteristics that could explain those results (point 5 and

6): 1) Jiang et al. [2] stated fingerprints of FVC2000

DB2A have a higher image quality than those of FVC2000

DB3A. At a lower PR, successful retrieval needs closer

similarity between the query and the candidates, which is

more sensitive to the image quality. Therefore, FVC2000

DB2A has better retrieval performance than FVC2000

DB3A at lower PR. However, FVC2000 DB2A has a worseretrieval performance than FVC2000 DB3A at higher PR

because of partial fingerprints whose core point is near the

image edge or out of the image. FVC2000 DB2A has more

such partial fingerprints than FVC2000 DB3A, which fails

to be retrieved even at high PR; 2) About fingerprint image

quality, Indrawan et al. [13] confirmed it by measurementusing NIST Fingerprint Image Quality (NFIQ) algorithm

[46]. NFIQ outputs the image quality value (where 1 is the

highest quality and 5 is the lowest quality) for 800 images

per data set. Percentage of images with quality 1 and 2 (two

highest image quality value) are about 88%, 26%, and 93%

for FVC2000 DB2A, FVC2000 DB3A, and FVC2002DB1A, respectively. Unlike Jiang et al. [2], Indrawan et al.

[13] has nothing to do with core point (not depend on it), so

its retrieval performance on those data sets is in accordance

to their image quality results by NFIQ, i.e. best result on

FVC2002 DB1A, follow by FVC2000 DB2A, and thenFVC2000 DB3A.

IV. CONCLUSION

In this paper, analysis has been conducted for severalaspects of publicly available fingerprint direct-access strategiesover three public data sets. It is including their advantages anddrawbacks, related works to unveil sub methods that constructoverall method, and systematic accuracy comparison which isindependent to the hardware platform. Accuracy comparisongave a thought about “a configuration standardization” sinceseveral different configurations were introduced that makehead to head comparison rather difficult to conduct. However,it could be concluded on this recent accuracy comparison, PRequal to 5% could be obtained at ER equal to 4%, 10%, and



1% each on FVC2000 DB2A, FVC2000 DB3A, and FVC2002DB1A by certain strategies with their certain configuration.

This analysis gives important foundation on providingknowledge for future possible combination of all the best thingfrom the other compared fingerprint direct access strategies.This foundation is for better future developments over newstrategies or future improvements over existing strategiesinclude authors’ work. Better future developments orimprovements is not intended only in area of fingerprint directaccess strategies, but also other data with same fashion ofdirect access mechanism whose final objective to obtain moreaccurate result with smaller area under ER/PR curve andachieved with faster search time.

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