DSpace at VNU: An Efficient Cascaded System for Latent Fingerprint Recognition

2013 IEEE RIVF International Conference on Computing & Communication Technologies Research, Innovation, and Vision for the Future (RIVF) An Efficient Cascaded System for Latent Fingerprint Recognition Nguyen Thi Huong Thuy1, Hoang Xuan Huan2, Nguyen Ngoc Ky1 and Le Minh Khoi2 General Department of Technique - Logistic, Vietnamese Ministry of Public Security huongthuykta@yahoo.com, kynguyen22@gmail.com Faculty of Information Technology, Vietnam National University – Hanoi, University of Engineering and Technology huanhx@vnu.edu.vn, khoilm@vnu.edu.vn Abstract - This paper proposes a cascaded scheme to improve the efficiency of latent fingerprint identification system In this scheme, the feature set of latent fingerprint such as finger codes, basic patterns, ridge counts and minutia with their local structures are sequentially exploited in four cascaded layers In the first layer, possible finger codes of latent fingerprint are recognized based on basic pattern features and then reordered to determine their database access priority In the second layer, its minutiae are extracted and assessed to affine matching in the third layer In the fourth layer, any case having too many candidates in the previous layer is further matched based on exploiting local structure in order to downsizing the result list On the verification layer, the minutiae information and local structure of corresponding pairs are presented to human experts for further verification Experimental results on C@FRIS database show that our proposed method obtains high matching accuracy and considerably low identification time Keywords: AFIS, C@FRIS, latent fingerprint identification, fingerprint verification, cascading, minutiae I INTRODUCTION Fingerprint-based identifications have been applied efficiently in forensics applications over a century [4] However, there are many open problems attracting researchers in this field [1]-[5], [7]-[9] While verification and automatic authentication for rolled and plain fingerprints have achieved tremendous progress, latent fingerprint recognition faces difficult problems [1], [7] Latent fingerprints from relative careless inadvertent individuality on objects are usually of poor quality because of noise and non-linear distortion Therefore, it is difficult to match them In addition, in Vietnam and many other countries, along with latent fingerprints collected at crime scenes, databases mainly store paper-thin fingerprints or scanned paper-thin fingerprints which are more difficult to process than sensor fingerprints In Automatic Fingerprint Identification System (AFIS), matching two fingerprint images is one of the main tasks It decides the performance of the system [7] In the literature, many matching techniques have already been proposed [2], [7] Nevertheless, the techniques yielding high performance usually require large amount of search time One of the prospective solutions to reduce the identification time is to apply cascading technique which integrates many algorithms from simple to complex into an AFIS [3], [13] This paper proposes a 4-layers cascaded architecture for latent fingerprint identification system In the first layer, latent fingerprint is recognized by its possible fingers [10] based on basic pattern features and then reordered (to determine the database access priority on the third layer); In the second layer, minutiae and their local ridge-valley structure will be extracted and assessed for affine matching in the third layer If the matching result is doubtful, matching algorithm based 978-1-4799-1350-3/13/$31.00 ©2013 IEEE on P-TPS model improved from [9] will be applied to decide before verification P-TPS matching helps eliminate the nonlinear distortion effectively In order to decrease identification time, fingerprints in database are organized and indexed by finger codes, basic fingerprint pattern Moreover, the minutiae matching process is parallelized on a computer cluster Experimental results on the database C@FRIS DB show that our new proposed system provides better outcomes than that of the earlier version of C@FRIS (built by research group of the Vietnamese Ministry of Public Security, awarded VIFOTEC 2008 and upgraded in 2009) The rest of the paper is organized as follows Section briefly introduces latent fingerprint recognition and some basic techniques for cascaded architecture The method applying P-TPS technique is described in Section The scheme of the cascaded system and the organization of the database for parallelized searching are highlighted in Section Section presents experimental results compared with C@FRIS system Finally, Section concludes the paper II LATENT FINGERPRINT RECOGNITION AND RELATED WORKS This section briefly introduces latent fingerprint identification system It also describes some techniques such as finger recognition, fingerprint classification, matching minutiae, TPS warping model A Latent fingerprint recognition and identification 1) Latent fingerprint matching problem The latent fingerprint matching problem is described as follows: Given a query fingerprint Iq (latent) and a fingerprint database, it is to determine whether the database contains the genuine fingerprint of this query fingerprint or not If yes, the system will display it Latent fingerprints in nature are often of poor quality and not complete as rolled/plain fingerprint Thus, it is difficult to use them as inputs for an automatic recognition system Therefore, the identification process is usually divided into two layers: identification by computer and visual verification by human being [7] The identification layer aims at finding the fingerprint images in the database which are most similar to Iq This layer is usually done by AFIS After having the images outputted by the first layer, the verification layer is to identify among them which one is genuine with Iq This layer is often performed by human experts, either with or without computer assistance Examiners often stop once getting the first match 2) Latent fingerprint identification system While automatic fingerprint identification systems work well with rolled/plain fingerprints, latent fingerprint identification still remains a challenging task and attracts 123 attention from the research community [1], [2] In order to solve the latent fingerprint problem with high accuracy and decreased search time, it is necessary to combine different techniques (see [13]) Classification and cascading is one of the most efficient methods which help downsize the matched fingerprint list from database Moreover, fast matching method helps remove most dissimilar fingerprints with Iq to focus on most similar fingerprints Specifically, a matching method needs to effectively process non-linear distortion This paper proposes an AFIS using cascaded architecture with the following components: fingerprint classification, finger recognition, parallelized minutiae matching based on reasonable organizing database for matching and verification assistance In the following sections, these components are going to be briefly introduced B Finger recognition based on fingerprint trace In order to determine the finger or the order of possible fingers of suspect which left the latent trace, K Nguyen Ngoc [10] proposed a statistic method using maximum a posteriori probability (MAP) for finger recognizing based on latent fingerprint trace Based on this method, the identification process can be conducted first on the suspected fingerprint instead of all fingerprints This significantly reduces the search time C Fingerprint classification In order to accelerate the identification process, fingerprint images are often classified into basic patterns according to local ridges and relative position of singular points [4], [6], [11], [12] Identification only applies to fingerprints of the same type with Iq in the database The FBI [7] proposed classifying fingerprints into three basic patterns: the arch, the loop and the whorl In order to improve fingerprint classification performance according to the FBI’s standard, Karu [4], [7] proposed a solution using Poincare index for detecting minutiae However, the drawback of this method is that it requires complete fingerprint and local ridge orientation of core region and delta region which need to be clear To eliminate the limitations, Wang [11] proposed a classification algorithm which is only based on core points and directions around the core point Nevertheless, an exact identification of the core points is required Our proposed system combines the two methods of Karu [4] and Wang [11] which are going to be described in Section 4.1 to classify a fingerprint D Minutiae based fingerprint matching Given a query fingerprint Iq and a template fingerprint It, it is to find out whether they originate from the same finger or not In all fingerprint matching algorithms [7], matching based on minutiae is simple but yet efficient and therefore is widely used 1) Minutiae based method In a fingerprint image, points representing discontinuities of fingerprint local structure such as end points, bifurcation points are called minutiae In order to match fingerprints using sets of minutiae, two fingerprint images Iq, It have to be pre-processed by extracting and assessing features 2) Matching scheme based on minutiae Let nt and nq be the number of minutiae on the query fingerprint and sample fingerprint, respectively Assume that there are n corresponding minutiae pairs found from two images The similarity of two fingerprint images is characterized by the measurement S(It,Iq) and given by the following formula: S(It,Iq) = n2/(nt×nq) (1) The simplest and most general transformation used in matching methods to align two images is the affine transformation However, due to the nonlinear distorted nature of latent fingerprints, the efficiency of this method is insufficient and is usually employed to determine initial corresponding minutiae pairs for advanced warping methods [2], [7] One of widely used warping transformations is ThinPlate-Spline (TPS) deformation model in [5] E Thin-Plate-Spline deformation model After having determined the n pairs of corresponding minutiae by using affine transformations for creating an initial set of landmark points, our system warps the image by the TPS model [5], [7] III PARTIAL TPS FINGERPRINT MATCHING METHOD In order to deal with the non-linear distortion problem, the authors in [9] proposed a partial TPS warping method using an additional technique to enrich the set of landmark points by appending more pseudo-minutiae belonging to the associated ridge-valley pair Our experimental results verified that this matching based on local point model helps solve the nonlinear distorted problem efficiently In this paper, P-TPS technique is used for building improved matching algorithm for doubtful fingerprint image pairs which are the outcomes of the affine matching IV CASCADED ARCHITECTURE AND PROCESSING DATA This section introduces the constituent components of the cascaded architecture and the identification It also highlights the organization of the fingerprint database A Components of the new system The system is made of four linked components/modules as described in Fig 1: i) Finger classification: Recognizing finger priority and classifying to basic patterns to match in the third and the fourth layer ii) Feature extraction: Extracting minutiae, ridge-valleys structures to be used for matching in the next layers iii) Affine matching: Performing minutiae matching by affine alignment as in Section 2.4 124 iv) P-TPS matching: Performing P-TPS matching according to the algorithm in [9] Among the above components, it is necessary to present more details about classification method in the first component Fingerprint classification Forensic fingerprints and registered fingerprints in database are classified into 10 types by combining the two methods of Karu [4] and Wang [11] If this procedure produces ambiguous outcomes, Karu’s method will be used Then, if the outcomes are still ambiguous, basic ridge will be taken into consideration If ridges at the core region are in good quality, Wang’s method is applied [11] Otherwise, delta points will be searched using Karu’s method [4] When the core region and the delta points are unclear, we use basic ridge to validate ambiguous cases This method helps eliminate incomplete and unclear minutiae Identifying basic ridge is quite straightforward by analyzing curves which are represented in vectorized form By doing this, the classification accuracy is increased up to 95% Nevertheless, it is still insufficient to apply the cascaded combination technique In order to improve the reliability, in ambiguous cases, fuzzy recognition technique type II will be employed If the system cannot classify into a specific class, it will display a list which is sorted in decreasing order of the classification reliability By doing this, the classification layer always provides the next layer with a controllable input B Cascaded identification scheme With the above components, the identification process is performed sequentially as below: Step 1: After the acquisition phase, latent fingerprint Iq is pre-processed and classified Ridge counts are calculated and the finger order is determined As latent fingerprints normally have a poor quality, it is difficult to extract minutiae automatically Therefore, the fingerprint image will be interactively edited by human experts to improve image quality This is done by the assistance of a graphic tool Based on this finger order, classified basic pattern and automatic preextracted features of the template fingerprint It are then retrieved from database for matching (in Step 4) Step 2: In the classification module, basic pattern of Iq is determined either automatically or manually If there are ambiguous outcomes, they are used for searching for the match by the priority of the reliability Afterwards, Iq is passed to the feature extraction module Step 3: In the feature extraction module [7], [14], the features of Iq including minutiae points and ridge-valley associated structure with quality map are extracted by interactive editing method The results are then passed to the affine matching module (Step 4) and the P-TPS matching module (Step 5) If Iq has such poor quality that the system cannot perform minutiae extraction on it, the process stops and delivers notification Step 4: In the affine matching module, the features of fingerprint It in the database are sequentially retrieved to match with the features of Iq (taken from Step3) for calculating the initial corresponding minutiae set and the similarity S(It,Iq): 4.1 If S(It,Iq) < Smin then the two fingerprints are not matched The system proceeds with the next fingerprint It in the database 4.2 If S(It,Iq) >Smax the system appends It to the output list Otherwise, that means S(It,Iq) ∈ [Smin, Smax], the system passes the corresponding minutiae pairs set and their associated ridge-valley pairs to P-TPS matching Step 5: P-TPS matching Continue with Step until all features in the database that have the same finger code and pattern with those of Iq are matched Step 6: Sorting the search result list according to priority: finger code, basic pattern code, ridge count, similarity The system verifies based on the priority and displays the results In the output list, corresponding minutiae pairs and associated ridge-valley pairs are displayed sequentially; each pair of fingerprint image Iq, It is sorted in decreasing order of the similarity of sub-patterns (based on finger code, basic pattern, ridge count) on display screen There are many useful tools for assisting human experts in verifying the results With the identification process above, the first three steps could run in parallel However, that would not improve the running time efficiently Therefore, this paper only proposes parallelized matching method for Step and Step These are the two steps that account for the most time So they are implemented by a computer cluster with many nodes process in parallel The cascaded architecture is described in Fig Fig 1: The cascaded architecture scheme C Organizing the database In applications such as identity card and criminal card, each fingerprint is represented as one record having the following basic fields: - Identity Card number; - Personal information fields (full name, date of birth, sex, address); - Finger code; - Basic pattern; - Left ridge count, middle ridge count, right ridge count, ridge density; - Minutiae set; - Fingerprint image (standard resolution 500 dpi, about MB for a 10-finger set); To accelerate the searching and matching processes, it is necessary to organize the fingerprint database in a sensible fashion In the database, fingerprints are indexed and organized hierarchically according to the following fields: fingerprint code, basic pattern, ridge counts and ridge density Information about minutiae points and associated ridge-valley 125 pair is stored with the corresponding fingerprint image for faster parallel searching The extraction of basic attributes from fingerprint with higher reliability for indexing remains an open research problem [2] To reduce the false rejection rate (FRR), fuzzy search method combining major code and minor codes for coding ambiguous attributes has been proposed For example, a ridge has whorl pattern with low ridge count can be classified incorrectly into a loop ridge Hence, we need to search both whorl ridge and loop ridge with major code corresponds former searched whorl pattern and minor code corresponds later searched loop pattern D Parallel matching For parallel matching solution, we setup a computer cluster system with the following components: i The server receives matching requests and performs searching according to the basic attributes It then splits the list into small pieces and distributes them to parallel processing nodes for minutiae matching ii Parallel processing nodes receive task and perform matching They deliver results in terms of a search result list to the server iii Workstations receive the search results from server They display the results to human experts who then perform the final verification V EXPERIMENTAL RESULTS Our experiment compares the performance of the new system with C@FRIS version 2009 A criminal identity database of C@FRIS system applied at the Police Office of Hanoi City contains 2.500.000 one-finger fingerprint cards with the standard resolution of 500 dpi The hardware system for performing the experiments consists of one mid-range server, five PCs linked together by the star network topology (parallelize with k=5) The performance of the system is determined by the length of identification result list and the search time In practice, the search time of C@FRIS system and that of the new system when they use sequential processing are equal, therefore we only compare the search time when both systems are running in the parallel processing mode Sixty-four latent fingerprints with average or quite good quality have been matched against the database The result list is sorted by finger priority order, basic pattern code order, ridge count order and the descending similarity (in the same group) The length of the search result list is the number of records on this list In practice, only 710% can be found in the database Hence, the length of the real list is equal to the number of records on searched fingerprint cards The remainder 90-93% unfound fingerprint traces are from people without a criminal record In those cases, examiners need to spend more time to verify until the end of the list Thus, sorting the result list and determining the maximum length of the real list for both found and unfound cases play an important role The experimental results show that the proposed method gains a high performance It increases search speed significantly In addition, it reduces on average 66.2% verification time per request compared with the case when the cascaded configuration is not applied VI CONCLUSION This paper proposes a cascaded architecture for latent fingerprint recognition system By applying combined fingerprint classification techniques, finger code recognition, ridge count, we were able to dramatically shorten the matching list Moreover, applying partial TPS matching method helps reduce the effect of non-linear distortion phenomenon Thanks to the parallel matching process, search time has been significantly decreased The experimental results show that the new system gains higher performance and better, response time in large database ACKNOWLEDGEMENT This work is partly supported by Vietnam National Foundation for Science and Technology Development REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] 126 A.K Jain, J Feng, “Latent fingerprint matching”, IEEE Trans Pattern Anal Intell, pp 88–100 (2011) T Y Jea (2005), “Minutiae based partial fingerprint recognition” PhD thesis of the University at Buffalo, the University of NewYork Q Jin, Z Shi, X Zhao and Y Wang (2004), “Cascading a couple of registration methods for a high accurate fingerprint verification system“, Proceedings of SINOBIOMERTRICS, Guangzhou, China, pp 490-497 K Karu and A Jain (1996), “Fingerprint classification“, Pattern Recognition, Vol.29, no.3, pp 389-404 J Li, S Tulyakov, Z Zhang, V Govindaraju (2008), “Fingerprint Matching Using Correlation and Thin-Plate Spline Deformation Model”, 2nd IEEE Conference on Biometrics: Theory, Applications, and Systems (BTAS 08), Washington, pp – S U Maheswari, Dr E Chandra (2012), “A Review Study on Fingerprint Classification Algorithm used for Fingerprint Identification and Recognition”, IJCST Vol no 1, pp 739-744 D Maltoni, D Maio, A K Jain, S Prabhakar (2009), “Handbook of fingerprint recognition”, Second ed, Springer-Verlag M A Medina-Pérez, M García-Borroto, A E Gutierrez-Rodriguez, L Altamirano-Robles (2012), “Improving Fingerprint Verification Using Minutiae Triplets”, Sensors, vol 12, pp 3418-3437 T.H.T Nguyen, H Hoang Xuan and K Nguyen Ngoc (2013), “An Efficient Method for Fingerprint Matching Based on Local Point Model”, Proc of the International Conference on Computing, Management and Telecommunications (ComManTEL2013), January 21-24, 2013, in Ho Chi Minh City, Vietnam, pp 334-339 K Nguyen Ngoc (1997) “A Fingerprint-based Method for predicting finger”, Journal of Public Security, No 1, pp 25-27 (in Vietnamese) S Wang, W W Zhang, Y S Wang (2002), “Fingerprint Classification by Directional Fields“, Fourth IEEE International Conference Multimodal Interface, Pittsburgh, PA, pp 395-399 Q Zhang, K Huang and; H Yan (2001), “Fingerprint Classification Based on Extraction and analysis of Singularities and Pseudo ridges“, Proceedings Selected papers from VIP2001, Sydney, Australia, pp 83-87 S Zia, S K Soni, S Sweta, P Mokal (2011), “A Casscaded Fingerprint Quality Assessment Scheme for Improved System Accuracy”, International Journal of Computer Science Issues, Vol 8, Issue 2, pp 449-455 Neurotechnology, Inc, Verifinger 4.2 SDK http://www.neurotechnology.com  ... corresponding minutiae pairs for advanced warping methods [2], [7] One of widely used warping transformations is ThinPlate-Spline (TPS) deformation model in [5] E Thin-Plate-Spline deformation model After... architecture for latent fingerprint recognition system By applying combined fingerprint classification techniques, finger code recognition, ridge count, we were able to dramatically shorten the matching... Fingerprint classification“, Pattern Recognition, Vol.29, no.3, pp 389-404 J Li, S Tulyakov, Z Zhang, V Govindaraju (2008), Fingerprint Matching Using Correlation and Thin-Plate Spline Deformation