A complete fingerprint matching algorithm on GPU for a large scale identification system

A complete fingerprint matching algorithm on GPU for a large scale identification system tài liệu, giáo án, bài giảng ,...

A Complete Fingerprint Matching Algorithm on GPU for a Large Scale Identification System Hong Hai Le, Ngoc Hoa Nguyen and Tri Thanh Nguyen Abstract &Fingerprints are most used biometrics features for identification Although state-of-the-art algorithms are very accurate, but the need for processing speed for databases with millions fingerprints are very demanding GPU devices are used widely in parallel computing tasks for their efficiency and low-cost Most approaches make use of GPU for the filtering process in a multi-stage matching system In this paper, we present a complete fingerprint matching algorithm on GPU Our approach uses minutia cylinder-code (MCC) representation with a global consolidation stage and a careful design to make it suitable for the architecture of GPU The result tested with GTX- 680 device shows that the proposed algorithm can perform 1.8 millions matches per second, making it applicable for real time identification systems with databases of millions fingerprints Keywords Fingerprint identification · Matching · Minutiae · MCC · GPU · CUDA Introduction Approaches to fingerprint matching algorithms which compare two given fingerprints and return a degree of similarity are often classified into three types: correlation-based matching, minutiae-based matching, and ridge feature-based matching The Fingerprint Verification Competitions (FVC) [2] shows that the minutiae-based matching is the most popular approach Minutiae are the points where the ridge continuity breaks and it is typically represented as a triplet (x, y, θ); where x and y represent the point coordinates and θ is the ridge direction at that point The task in the minutiae-based matching approach is finding the maximum number of matching minutiae pairs in two given fingerprints Figure shows matches between two fingerprints based on minutiae H.H Le() · N.H Nguyen · T.T Nguyen Vietnam National University of Hanoi, 144 Xuan Thuy, Hanoi, Vietnam e-mail: {hailh,hoann,ntthanh}@vnu.edu.vn © Springer Science+Business Media Singapore 2016 K.J Kim and N Joukov (eds.), Information Science and Applications (ICISA) 2016, Lecture Notes in Electrical Engineering 376, DOI: 10.1007/978-981-10-0557-2_67 679 680 H.H Le et al Most minutiae-based matching algorithms consist of two steps: perform a local structure matching and followed by a consolidation stage The local structure matching allows to quickly find pairs of minutiae that can be matched locally and can be candidates for aligning between the two fingerprints Local structures are normally invariant to the fingerprint rotation and translation Local structures of minutiae are typically represented by neighboring minutiae [3,11], ridges [7,8], orientations [9,10], or combination of these [6] Recently, Minutia Cylinder-Code (MCC) representation [4] shows a good performance in both accuracy and speed of fingerprint matching The aim of the consolidation stage is to check whether the local matching minutiae pairs still satisfy at the global alignment level of two fingerprints There are basically two methods to increase the speed of a fingerprint identification system: reducing the total number of fingerprint comparisons (through fingerprint classification [16, 17], pre-filtering or multistage matching [18,19]), or using parallel architectures [14] Graphic Processing Unit (GPU) has been proven to be a very useful tool to accelerate the processing speed of computationally intensive algorithms These devices introduce massive parallelism in the calculations and apply successfully in fields such as artificial intelligence [21,22], simulation [23] and bioinformatics [24] Recently, having reports for applying GPU for MCC fingerprint matching like the works of Gutierrez et al [12], Capelli et al [13] Most approaches make use of GPU for the filtering process After that, more accurate matching algorithms on CPU for remaining fingerprint candidates are used In this paper, we propose a different approach to adapt complete stages of the fingerprint matching algorithm based on MCC representation for GPU, the proposal is suitable with GPU computing architecture, making it easy for implementation Fig Fingerprint matching based on minutiae The rest of the paper is organized as follows: in section 2, we review the stages of the fingerprint matching algorithm based on MCC GPU programming model is briefly described in section Section describes our adaption MCC for GPU Section details the experimental results over FVC 2002 DB database A Complete Fingerprint Matching Algorithm on GPU Matching Algorithm Based on MCC 2.1 Local Structure Matching 681 Minutia Cylinder-Code (MCC) representation [4] shows a good performance in both accuracy and speed of the fingerprint matching Each minutia is represented by a cylinder feature This cylinder which is centered at the minutia, has a fixed radius R, and a height of 2π Each cylinder is divided into × × cells as shown in Figure defines the resolution of the discretized 2D space around represents the number divisions applied to the minutia ( × ) and height of the cylinder (2π) which represents the angular distance The contribution of each minutia to a cell (of the cylinder corresponding to a given minutia ), depends both on: spatial information (how much is close to the center of the cell), and directional information (how much the directional difference between and ), is similar to the directional difference associated to the section where the cell lies) In other words, the value of a cell represents the likelihood of finding minutiae that are close to the cell and whose directional difference with respect to Fig Structure of a cylinder [12] Once a cylinder is built for minutia , it can be simply treated as a single feature vector With a negligible loss of accuracy [4], each element of the feature vector can be stored as a bit A simple but effective similarity measure between two bit vectors of cylinder and and of cylinder is described in Formula [4] || ⨁ || 1− , ≤ (1) , = || || + || || ℎ Where  ⨁ represents the bitwise XOR operator;  || || represents the Euclidean distance;  ( , is the difference between the angles of the two minutia and ; is the maximum rotation threshold allowed between two fingerprints  682 H.H Le et al With the cylinder set of the two fingerprints ( and ) to be matched, a local matching process is started on every pair of cylinders using Formula and the results are stored in a matrix After that most approaches for GPU [12,13] use Local Similarity Sort (LSS) technique which sorts all values of the matrix and computes the average of the top values This does not guarantee that these top minutiae pairs are matched with each other The consolidation stage presented in the next section is used to check whether the local matched minutiae pairs still satisfy at the global alignment level of two fingerprints 2.2 Consolidation Stage The simplest consolidation approach uses the local matched minutiae pair having the maximum similarity value in order to align the fingerprint and for the global matching step After the alignment, all local matched pairs are verified whether they are still matched by the following constraints: - The Euclidean distance between the two minutiae does not exceed threshold ts The difference between the two minutiae angles does not exceed threshold tθ The two parameters ts and tθ represent the tolerance window and their value can be determined by experiments For example, in TK algorithm [10], the distance threshold ts= 12 and the angle threshold tθ= π/6 brought a good result of fingerprint matching However, the transformation on the minutiae pair having maximum similarity value may not be the best transformation at the global level Several authors have adopted multiple candidate transformations for the alignments Finally, the transformation that maximizes the number of global matching minutiae pairs will be chosen For instance, Medina et al [11] reduced the number of local matching minutiae pairs by for each minutia p and minutia q, selecting only minutia that maximizes their similarity values, then perform the transformation for each minutiae pair in the reduced set Feng et al [6] sorted minutiae pairs by descending similarity values and chose top minutia pairs for the transformation Normally, these approaches allow to get better accuracy than that of the single transformation approach Graphics Processing Units The Compute Unified Device Architecture (CUDA) is one of the most widelyadopted frameworks for GPU; CUDA is a hardware and software architecture that enables NVIDIA GPU to execute parallel kernels written in C The physical architecture of CUDA-enabled GPU consists of a set of Streaming Multiprocessors (SM), each containing 32 cores for SIMD (Single Instruction Multi Data) In the CUDA programming model, a CUDA kernel is executed in parallel across a set of threads, which are organized into blocks All threads of the same block are executed A Complete Fingerprint Matching Algorithm on GPU 683 on the same SM and share the limited memory resources of that multiprocessor The maximum number of threads in a block cannot be too big (1024 in the GPU used in this work) However, a kernel can be executed by multiple, equally-sized blocks, forming a grid: the total number of threads is then equal to the number of blocks times the number of threads per block (Fig 3) Each SM schedules and executes threads in groups of 32 parallel threads (being 32 the number of cores in a SM) called warps A warp executes one common instruction at a time, so full efficiency is realized when all 32 threads of a warp synchronize their execution path If threads of the same warp take different paths (due to flow control instructions), they have to wait for each other It is important to make GPU threads are extremely lightweight CUDA threads have access to various memory types (Fig 3): each thread has its registers, which are the fastest memory, and its private local memory (which is slower); each block has small shared memory accessible to all threads of the block and with the same lifetime of the block; all threads have access to the global memory: the largest and slowest memory, which is used for communication between different blocks and with the host When a warp executes an instruction that accesses global memory, it coalesces the memory accesses of the threads within the warp into one or more of these memory transactions, depending on the size of the word accessed by each thread and the distribution of the memory addresses across the threads [15] Therefore a very important optimization in CUDA is to ensure that global memory accesses are as much coalesced as possible Fig CUDA: grid, blocks, threads, and the various memory spaces [15] 684 H.H Le et al Adapting Complete Fingerprint Matching to GPU For identifying a query fingerprint in a database of template fingerprints { , …, } using matching algorithm based on MCC representation, the first local structure matching stage calculates similarity matrices, after that similarity score set = { , , , } is calculated Figure demonstrates these calculating steps When adapting the algorithm to GPU, the aim is to maximize active threads Threads are grouped by wraps Each of which contains 32 threads Because of the variable number of minutiae of fingerprints, to avoid divergence between threads in the warps, approaches [12,13] divided the algorithm mainly into separate kernel GPU calls The first kernel GPU call is to calculate all similarity matrices The second GPU call is to calculate score from similarity matrixes When dividing the algorithm into separate calls, it needs to transfer data between kernel calls and some advantages of the GPU architecture like share memory is not utilized [13] used a very careful design algorithm and ad hoc technique to translate the similarity matrix to a fix size Fig Calculating for fingerprint identification process using MCC [13] [12,13] did not use consolidation stage to calculate score set = { , , , }, they used Local Similarity Sort (LSS) technique which sorts all values of the matrix and computes the average of the top values This does not guarantee that these top minutiae pairs are matched with each other After that, more accurate matching algorithms on CPU for remaining fingerprint candidates are used Our approach is based on a view using 32 minutiae for each fingerprint is enough for the matching process From statistics of FVC 2002 fingerprint databases, the average number of minutiae of each fingerprint is 30, and the average number of matches for a genuine matching is In our algorithm, we use all minutiae for calculating cylinders of the fingerprint after that we choose 32 minutiae with cylinder having maximum number of value Minutia with cylinder having less value tends to be outline of the fingerprint All minutiae are used to calculate cylinders so the bit vectors of cylinders not be affected A Complete Fingerprint Matching Algorithm on GPU : − Template fingperprints { , , , } − A query fingerprint : −Matching score set = { , , , } Kernel execution configuration: − 32 threads per block, blocks ℎ ℎ [32] Share memory Share memory [32] Share memory ℎ Block and thread index of the current thread , //Local structure matching stage : the cylinder of minutia of template : the angle of minutia of template For = to 32 : the cylinder of minutia of query fingerprint : the angle of minutia of query fingerprint )≤ ) If( ( , = ( , ) [ ], updateMax( ) [ ], ) updateMax( End If 10 End For 11 syncthreads() //Consolidation stage =0 12 ℎ 13 For = to 32 14 If and ( , ) pair of , ( [ [ ] ) pair of ] 15 ℎ ++ 16 End If 17 End For 18 atomicMax(maxMatching, ℎ ) 19 syncthreads(); 19 If( == 0) 20 = ℎ /(32 ∗ 32) Fig Complete fingerprint matching on GPU 685 ℎ 686 H.H Le et al By using our approach, all the similarity matrices in Figure have the same size of 32x32 We use one block for matching with , each block has 32 threads Each thread of the block is used to calculate a column in the similarity matrix and to find the maximum value in that column The minutiae pairs which have maximum values are checked with each other in the consolidation stage to guarantee they are matched The details of the algorithm are presented in the Figure       All the cylinder and angle of the templates of the databases are previously loaded into the global memory of GPU is used to calculate the similarity The GPU block with index of between fingerprint template of the fingerprint database with the query fingerprint Each thread with the index of of the block is used to calculate the maximum value of the matrix column using the loop at line and store that value in the array , in line is calculated by using Formula The similarity ℎ () function in line 11 is a barrier for all the threads of the block, after that all the results of the threads of the block are available for the consolidation stage () function in line 18 helps to avoid race condition occurs when two or more threads of the block update the share variable ℎ at the same time The similarity score is calculated in line 20 by the first thread of the block using maximum matching value found from all threads of the block Experimental Results In order to evaluate our proposed approaches, we used the FVC 2002 DB1 database to carry out experiments The minutiae extraction and creating MCC cylinders process used tools from Pérez et al [25] The MCC templates of fingerprints were stored on disk and used for the experiments For evaluating the accuracy of the proposed algorithm, the result of the proposed algorithm is compared to the result of the MCC baseline in which all minutiae are used for the matching process We achieved an EER of 1.34% against a 1.26% of MCC-baseline These are certainly minor differences and can be accepted in real world applications For evaluating the speed of the proposed algorithm, we carried out all the experiments on a GTX GPU, an NVIDIA GeForce GTX 680 with 1536 CUDA cores, Kepler Architecture and 2GB of memory FVC 2002 database was scaled to different database sizes (ranging from 10000 to 200 000) to study how the GPU based algorithm scaled with the database size 10 input fingerprints were randomly selected to be identified Table shows the result of experiments with databases with different sizes A Complete Fingerprint Matching Algorithm on GPU 687 Table Execution time of the first ten queriess with different database sizes DB size 10000 50000 100000 150000 200000 Time (ms) 58 284 567 850 1105 Throughput (KMPS) 1724 1760 1763 1764 1809 At larger DB sizes, throughput of the proposed algorithm is stable at 1.8 millions matches per second, no scalability issues were found It is higher than the result reported for previously published GPU algorithm [12], which gains 55.7 KMPS on a single GPU device, which is the same as our device Though [12] used a different fingerprint database in the experiments, the average number of minutiae of a fingerprint is quite stable Conclusions This paper proposes an approach of adapting the complete stages of the fingerprint matching algorithm based on MCC on GPU Our approach uses all minutiae for calculating cylinders, but choosing 32 minutiae for matching process, that makes the approach actually fit well with the GPU computing architecture The proposed method does not affect the accuracy of the original algorithm The speed of the adapting algorithm gains state-of-the-art result The proposed approach can be easily scaled-up thus makes it possible to implement large-scale fingerprint identification systems with inexpensive hardware References Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition Springer, London (2009) Cappelli, R., Maio, D., Maltoni, D., Wayman, J.L., Jain, A.K.: Performance evaluation of fingerprint verification systems IEEE Trans Pattern Anal Mach Intell 28, 3–18 (2006) Chikkerur, 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