Latent Fingerprint Indexing - Computer Science and Engineering

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prints in the background database. The normalized scores ′ = {s ′ 1,s ′ 2,...,s ′ N R } is obtained ass ′ i =s i − min(s)max(s) − min(s) . (9)The fusion is based on the saliency of individual features.The indexing performance of triplets, MCC and orientationfield descriptor is comparable; thus, with proper normalization,the simple sum rule is successful [15]. Singular pointsare not always present in fingerprints, especially in latentsthat usually contain only a partial area. Therefore, the indexingscore computed from the other features is updatedbased on whether singular points are available in both theprints being compared.Given the indexing scores from the constrained triplets(S T ), MCC indexing (S M ), and orientation field descriptor(S O ), we obtain locally normalized scores S T ′ ,S′ M andS O ′ . Given the indexing scores from singular points (S SP ∈[0, 1]) and ridge period (S R ∈ [0, 1]), the final indexingscore S is computed asS =(S ′ T + S ′ M + S ′ O) × (1 + S SP ) × S R . (10)The final indexing score S is used to order the backgrounddatabase such that, for each latent, the top-k referenceprints that are most similar to the latent print are retrieved.5. Experimental Results5.1. DatabasesIndexing experiments were performed on the only publiclyavailable latent fingerprint database, NIST SD27. Thisdatabase consists of 258 latent fingerprint images from operationalscenarios (i.e. latents collected from crime scenes)and 258 rolled prints corresponding to the mates of the latentfingerprints. We used 500 ppi resolution for both latentsand rolled prints. Minutiae marked by latent examiners areprovided with this database.The purpose of latent fingerprint indexing is to efficientlyfilter out a large portion of the background database so thatthe speed of matching stage is enhanced while maintainingthe matching accuracy. To show the effectiveness of theproposed indexing scheme, we build a large backgrounddatabase of 267,258 rolled fingerprints. This backgrounddatabase includes the 258 rolled prints from NIST SD27,27,000 rolled prints from NIST SD14, and 240K rolledprints from a database provided by the Michigan State Police.5.2. Indexing ResultsIndexing performance is usually measured by plottingthe hit rate vs. penetration rate. A hit rate at a given penetrationrate p refers to the fraction of search prints for whichthe correct mates are retrieved within p% of the backgrounddatabase (penetration rate). The desirable outcome is highhit rates at low penetration rates with high computationalefficiency.As a baseline, for the NIST SD27 database with backgroundof 267,258 rolled prints, the hit rates at the fixedpenetration rate of 20% are 62.0%, 73.6%, and 74.0% forbasic triplets, MCC indexing and the constrained triplets,respectively.Fig. 4 shows the performance of the proposed approachalong with the performance of two approaches proposed in[6] and [21]. The results of the algorithm in [21] shownhere are based on an executable file provided by the authors[21], applied to the same database and features usedin our approach. The hit rate at a penetration rate of 39%reported in [6] is shown here for Feng and Jain 2008, whichis based on a much smaller background database of 10,258rolled prints. Note that our approach cannot be directlycompared with Feng and Jain’s approach [6] because theyused a much smaller background database size compared toour approach and Yuan et al.’s approach [21]. However, inour experiments we observed that the differences in backgrounddatabase size do not largely affect the hit rates (onlyby 2-3%). Thus, if we fix the penetration rate to 39%, bothperformances are comparable.Hit Rate (%)1009080706050Feng and Jain 200840Yuan et al. 2012Proposed Approach300 10 20 30 40 50Penetration Rate (%)Figure 4. Comparison of indexing performance of the proposedapproach against Yuan et al. [21] and Feng and Jain [6]. Notethat Feng and Jain [6] used much smaller background databaseof 10,258 prints versus the proposed approach that uses 267,258prints in the background database. Further, Feng and Jain [6] didnot provide the complete hit rate vs. Penetration rate curve, butonly a single point (∙).Given that the proposed approach combines differentfeatures and techniques for indexing, Table 2 shows the incrementalcontributions to hit rates with the addition of differentfeatures at a fixed penetration rate of 20%. It canbe observed that the addition of features steadily improvesthe latent indexing performance, from 74% hit rate for justthe CTrip feature to 90.3% hit rate by including all the fivefeatures for indexing.The proposed approach is more suitable for latent index-
Table 2. Increase in hit rate (HR) at a fixed penetration rate (PR)(20%) as we incrementally add features for indexing (from left toright), where CTrip is constrained triplets, OFDesc is orientationfield descriptor indexing, SP is singular points, RP is ridge period.CTrip OFDesc MCC SP RP74.0% 81.8% 84.5% 87.6% 90.3%ing. For indexing of reference prints, the state of the artindexing methods [5] already report very high hit rate atvery low penetration rate. For example, the indexing performanceof MCC applied on NIST SD4 (the 2,000 secondimpressions are searched against the 2,000 first impressionsand 10,000 reference prints from the Michigan State Policedatabase) shows a hit rate of 97.8% at 5% penetration rate.Including additional features do not further improve the indexingperformance in this case.In order to further evaluate the performance of the proposedindexing algorithm, we also analyzed the influenceof the filtering on the latent matching performance. For thispurpose, two fingerprint matchers were used (one in-housematcher [17] and one COTS matcher, referred as Matcher1 and Matcher 2, respectively). The input to the matchersconsisted of manually marked minutiae in the latents andautomatically extracted minutiae in the rolled prints, whichis the same set of minutiae that was used in the indexingexperiments. For this experiment, for computational efficiency,we used a smaller background database of 27,258rolled prints (258 from NIST SD27 and 27K from NISTSD14). For both the matchers, the overall latent matchingperformance on NIST SD27 before and after filtering out80% of the background database (penetration rate of 20%)is maintained (the total computation time for the search isreduced by a factor of 5), as shown in Fig. 5. This is dueto the fact that although the indexing is not perfect, most ofthe errors made by our indexing scheme are also errors inthe latent matching stage as well.Identification Rate (%)7570656055Matcher 1 before filteringMatcher 1 after filtering50Matcher 2 before filteringMatcher 2 after filtering0 20 40 60 80 100RankFigure 5. Latent matching performance before and after applyingthe proposed indexing to filter out 80% of the backgrounddatabase (20% penetration rate). Note that the overall matchingperformance is maintained after filtering.Two latents and their true mates are shown in Fig. 6. Inthe first case (Fig. 6(a)), the retrieval rank of the true matewas improved from 3 to 1 after the filtering was applieddue to the exclusion of the two rolled prints ranked first andsecond in the matching stage. In the second case (Fig. 6(b)),the true mate was not retrieved among the top-100 retrievalsat a 20% penetration rate. This was due to missing minutiaeand limited number of triplets in the latent, which made thetrue mate retrieval rank for MCC and triplets low, althoughthe true mate retrieval rank for orientation field descriptorwas very good. Furthermore, the retrieval rank of the truemate before filtering was 543, which means that it was notin the top-100 candidate list before the filtering.(a)(b)Figure 6. Examples of latents and their true mates in which thefiltering applied at 20% penetration rate (a) improved and (b) degradedthe true mate retrieval rank (rank 3 to rank 1, and rank 543to exclusion, respectively).5.3. Implementation DetailsThe triplet indexing algorithm, singular point and ridgeperiod estimation modules are implemented in Matlab;MCC SDK (implemented in C#) is provided by the authorsof [5] and can be downloaded from their webpage 1 ;theorientation field descriptor-based indexing algorithm is implementedin C++. MCC indexing timing was obtained on aPC (Intel(R) Core(TM)2 Quad CPU Q9650 3.00GHz, 3GBmemory), and the timings for all the other modules wereobtained on a server with 48 cores (AMD Opteron(tm) Processor6176, 2.3GHz, 193GB memory); no parallel computingresource was used, thus only one core was used whenmeasuring the computation time.Since the modules are all independent and can be run inparallel, we estimate the indexing efficiency based on the1 http://biolab.csr.unibo.it/
Page 1 and 2: Latent Fingerprint Indexing: Fusion
Page 3 and 4: RolledPlainLatentLiang et al. [13]
Page 5: N is the number of minutiae, o i =

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