Partially Acquired Fingerprint Recognition Using Correlation Based Technique.

Transcription

1 Partially Acquired Fingerprint Recognition Using Correlation Based Technique. 1 Hrushikesh G. Manoli, 2 K.S. Tiwari 1,2, Dept. of Electronics and Telecommunication Engineering, Modern Education Society s College of Engineering, Pune 1 hrushikeshmanoli@yahoo.in, 2 kanchan.tiwari@mescoepune.org Abstract-- Fingerprints are now-a-days the most commonly used biometric for the identification/verification of people. The Fingerprint Recognition method consists of four stages. First stage is the acquisition stage where the fingerprint image is captured, the second is pre-processing stage for enhancement i.e. noise removal, binarization, and morphological processing of fingerprint image. The third stage is the feature extraction stage, where minutiae are given priority ahead of singularities, as the singularities may be absent in a partial fingerprint. DWT is also included as an extraction technique. The last stage is matching stage, that is, to match the minutiae points with help of generated database in which similarity and distance measure are calculated. Two fingerprints should be aligned properly, so that the similarity between them is measured, but the use of DWT overcomes this constraint due to its rotation invariance. The usual fingerprint recognition methods (minutiae-based) only use limited information that is- singularities and minutiae, affecting the reliability of the system s output. The reliability is found to be 80.55% for 61.1% of entire fingerprint, as the partial fingerprint. To surmount this, in this paper, a hybrid region-based fingerprint recognition method (achieved using DWT) is proposed in which the fingerprints are decomposed into sub-bands and their correlation coefficients will be computed. Index terms Partial fingerprint, minutiae, singularities, wavelet decomposition. I. INTRODUCTION Fingerprint identification is one of the most well-known and accepted biometrics [1]. Because of their uniqueness and consistency over time, fingerprints have been used for identification for over ten decades, and are becoming automated (i.e. all biometrics) due to advancements in computing technology. Fingerprint identification is popular amongst masses due to its ease of acquisition, the multiple sources (ten fingers) being available for collection and it is stated legal by law as a proof of human biometric. This fingerprint recognition technique is used from over a century ago due to its individuality and consistency throughout a person s life. The first step to recognize a fingerprint is registration, which involves enrolling the biometric data to a sensor which saves it as a template in the database. Then fingerprint recognition undergoes verification process or identification process which depends on its field of application. If the system is built for verification then the person s fingerprint is checked from the various templates saved with help of suitable algorithm [3]. This is known as one to one matching. It just compares the claimant s fingerprint to the one registered against that name in the database. If the system is built for identification, then the fingerprint obtained from a person is compared with all the fingerprint templates that are already stored in the database. This is known as one to N matching. It is used in the process of finding an unknown person by checking his fingerprints.. Fingerprint categorization is an important sub-problem for the Automatic Fingerprint Identification System (AFIS) and Automatic Fingerprint Recognition System (AFRS) [2]. This database is typically very large (e.g., several millions of fingerprints) that is used for many forensic and civilian based applications. For such huge databases, the identification process may require a lot of time. The speed of the validation process may be increased by reducing the quantity of comparisons that are necessary to be done. This can be achieved by partitioning the fingerprint database into various categories. The identification process then becomes much simpler, as it is compared with only those fingerprints that are in the same category of the database, which is generated using the properties of fingerprint. The popular Henry s Classification scheme divides a fingerprint pattern into three major classes or patterns namely Arch, Loop and Whorl. Fingerprint recognition is mostly for identification of people as compared to the various other biometric techniques. This is because of many reasons such as simplicity of acquisition, high distinction and persistence over time [10]. Also the fingerprint sensors are smaller and cheaper as compared to other biometric sensors. This bio-system can be used for detection of a person by using his characteristic properties (behavioral or biological). The Biological characteristics are related to that of bodily parts such as fingerprint, face, retina, iris, speech etc. The applications are in law enforcement system, access control, border management system, IT security and so on. The behavioral characteristics are the ones related to actions performed by a person, examples are voicenotes, keystroke-scan, and signature pressure scan. The fingerprint contains ridges and valleys on the surface of 60

2 the finger. The line denotes a ridge and the space between them is called as a valley as shown in Fig.1. The core and delta are called as the singularities and ridge endings, bifurcations etc. are called as the minutiae. Fig.1: Properties of fingerprints II. LITERATURE SURVEY Even though the exclusivity of fingerprint is evaluated with help of manual assessment (by experts), the study of formation of friction ridge has proved its consistency. Since decades, fingerprints are popularly being used and linked with police forensics, but with time they are very popularly used in mobile applications, security measures for banks and in biometric attendance system too. Even if, many efforts have been taken in the past three decades towards building a consistent automatic recognition machines, these systems are still un-ideal or contain some loopholes. A number of factors that act as hurdles in the way of building an ideally working system are- inadequacy of dependable pattern extraction methods, complexity in correct alignment of fingerprints and defining a dependable resemblance measurement amid fingerprints as well. Above all, the uniqueness of fingerprints is accepted by the standards worldwide. However, recognition of fingerprints is still not easy to work upon, primarily because of the considerable intraclass (a.k.a. within-finger) variation and large inter-class (a.k.a. between-finger) similarity in fingerprints. Intraclass variation is the case where the fingerprints somewhat differ every time they are taken. So multiple imprints of the same finger may not always be the same. In a similar way, inter-class similarity is the case when the imprints from the different fingerprints are somewhat same [4]. Intra-class variation generally occurs due to sensor noise, non-linear distortion, partial overlap and purposeful changing of finger impressions. Nonlinear distortion which is generated during fingerprint sensing is one of the most important factors for intraclass variability. It is caused due to the application of unequal pressure against the surface of fingerprint scanner. The technique of sensing, imprints the three dimensional profile of a finger onto the two dimensional surface of the sensor. Thus, due to elasticity of the skin, the non-linear distortion is introduced by taking multiple impressions of the finger. Fingerprint images may also contain noise, especially salt and pepper type of noise, is primarily due to the dust particles on the fingerprints [5]. This situation is encountered frequently and needs to be dealt with. Researchers projected various fingerprint matching techniques which can be coarsely categorized into three major groups as given by Maltoni et.al. in reference [4]. They are Minutiae-Based, Non-Minutiae- Based and Correlation-Based. All the fingerprint matching methods can be roughly categorized into the mentioned groups based on the features they extract from the fingerprints. Fingerprint features can also be categorized into three major levels [4]. Level-1 features (general patterns) are the macro information on the fingerprint such as ridge flow and ridge orientation. The level-1 features are usually used to categorize the fingerprints into particular classes or patterns such as loop, arch, tented arch, etc. Level-2 features consist of the minutiae, which are nothing but ridge bifurcations and endings. Level-3 features contain all dimensional attributes of the ridge such as ridge shape, width, pores, early ridges, breaks, creases, scars, and other permanent details (given in Jain et al., 2007). Regarding fingerprint features, it should be noted that some features could be highly-discriminative but are very sensitive to the quality of the images. Usually, minutiae features have these types of properties. Minutiae are defined as the points that a ridge ends or disjoints, therefore, minutiae extraction in low quality images will lead to detecting false minutiae due to the unclear ridges and valleys. The point where the ridge ends at the periphery of the image (as the image itself ends there) is also taken as a ridge ending and hence should be categorized under false minutiae. The Region of Interest helps to cut-off these points. Also, a threshold Euclidean distance needs to be set. Another disadvantage of Minutiae-based approaches is that they only use limited available information on the fingerprint. Pankati et al. claimed that extracted information in minutiae-based methods is limited, and algorithm developers should explore the use of non-minutiae based information as well. This problem becomes more serious when dealing with partial fingerprints since some of the remaining useful information is not used by minutiaebased methods. Typically, in a small area of a low quality fingerprint, only 4-5 minutiae may exist and in that case, minutiae-based methods will not work satisfactorily due to limitation in providing fingerprint discriminative information. On the other hand, grey-levels of the fingerprint image are used as information by the correlation based methods [5]. These methods take account of all spatial properties of a fingerprint that include micro characteristics such as minutiae, macro properties such as reference points, and also ridge thickness, ridge shape etc. A lot of information is hidden into the grey-levels, that are much more elaborative and distinct (valley region details), keeping aside the minutia. Furthermore, in correlation-based methods false/missed minutiae do not degrade the matching performance and even no hard decision needs to be made on the searching for minutiae pairs. Correlation-based methods are also capable of 61

3 dealing with low quality images. Although Correlationbased methods have a higher reliability, their main drawback is their high computational cost. To overcome this issue different strategies have been proposed. One of these strategies is to use an appropriate region selection for comparison purposes. Moreover, the computation required to compute cross-correlation can also be achieved in Fourier domain. Finally, computing correlation of the local regions of the images can be performed in parallel and hence is favorable for FPGA implementation as it can exploit the concurrency of the system. To improve the performance of correlation algorithms, [6] Jain A.K et al. proposed a method based on normalized cross-correlation. Their method involves extracting the common region between two fingerprints (as one single region) and computing the correlation of the common region. The highest correlation coefficient value is considered as the measure of degree of similarity between two fingerprints. The main drawback of this method is the reliability of the algorithm for alignment of images. This process is done manually by aligning the points of interest with the original image. By doing so, the fingerprint aligning accuracy improves significantly. However, such strategy for alignment is impractical in AFIS (Automated Fingerprint Identification System), which demands processing of huge quantity of database pictures. In addition, accurately aligning the fingerprints can significantly improve the system performance. Another drawback of this technique is regarding non-linear distortion. Say, if we apply the measure of similarity globally, to the entire image, the end results again consist of non-linear distortion. Reference [6] proposed a hybrid method using correlation of minutiae. Their method is based upon calculating the correlation between sub-regions around the minutiae co-ordinates and then computing the mean of correlation as the concluding degree of similarity between those fingerprints. Even though this method could adequately deal with this non-linearity problem, it has a limitation of being reliant on the ridge endings and bifurcations. Thus purely minutiae related techniques use very little information from the available image and orientation errors also occur frequently. Nandakumar and Jain have also written that the greylevel information of the pixels around the minutiae point contains richer information about the local region than attributes of the minutiae points. III. SYSTEM DESCRIPTION A. Proposed System: The proposed flow diagram for the fingerprint recognition system is as shown in Fig.2. Fig.2: Proposed block diagram of the system The aforesaid method is implemented by following the key steps given below-- Fingerprint enhancement, image decomposition with template evaluation, and calculating the degree of similarity. As stated in various techniques, the fingerprints are filtered, enhanced and segmented as the elementary step in this method. By doing a pixel level assessment of the fingerprints, the entire image is covered step by step, covering small local regions. For computing the correlation of the corresponding regions, the translation difference is dealt with the use of sliding window method. Hence, the images are must be aligned with respect to rotation for minutiae based methods. Similar rotary placement of two fingerprint images is nothing but rotating of fingerprint image till the point at which they achieve maximum similarity with respect to orientation. For partial fingerprints, there can exist a small overlie between the two fingerprints; therefore an appropriate tactic should be used which aligns them. Image-based approaches do not require the extensive pre-processing. It means the whole image as an input to extract the features. Wan Azizun Wan Adnan et al., in 2004 proposed a simple technique for fingerprint recognition which matches the fingerprint images based on the features extracted in the wavelet transform domain. Digitized image of fingerprint is preprocessed to obtain the binary image. Then thinning is carried out such that the thickness of the ridge is reduced to a single pixel. This is often termed as skeleton or infinite thinning in codes. Then the minutiae are extracted that occur in Region of Interest (ROI). This is done by using 8-neigbourhood algorithm [3]. After that the spurious minutiae are removed [7]. These points are then recorded in the database. In next part, the two dimensional Discrete Wavelet Transform is applied to the pre-processed image. Finally, a wavelet-based 4 x 1 feature row vector is obtained. This feature vector has information about the slopes of image. Identification can be made by comparing this feature vector with other fingerprint features. 62

4 B. Wavelets and pattern recognition approach: Image recognition using pattern matching techniques gives less information about the texture as images are analyzed at single scales. The use of wavelet in pattern recognition approach improves the classification accuracy as the images can be analyzed by multi-scale representation of texture. Wavelet s direction resolving property can be used to extract information in three directions (horizontal, vertical and diagonal) from texture which increases the recognition rates. So by the use of multi-resolution property of the wavelets more information of the texture can be obtained. For fingerprint recognition, the algorithm follows steps: Step1: Raw image is pre-processed to obtain binarized data form. Step2: 2D-DWT is performed on binarized image to produce 1D co-efficient plot (large scale, high frequency). Step3: 1D-DWT performed in step1 for a discrete representation at different scale. Step4: Stores particular fingerprint image in matrix form in database for later comparison Step5: Tolerance within slope set the threshold of verification for matching input template. Haar wavelet is used for the decomposition of the image into subbands. These sub-bands signify the approximation (LL), horizontal (HL), vertical (LH) and diagonal (HH) components of the original image. Thus a 3- level decomposition may be used for 2D-DWT. This algorithm is robust as DWT is rotation invariant transform. The flow of this algorithm is as shown in Fig.3. IV. RESULTS A. Pre-processing and parameter extraction of the image: The fingerprints were taken from FVC DB_1 data-base. Coding for pre-processing was done using Matlab software. This test image is called as Sample Fingerprint Image (SFI). Accordingly the steps followed are- Low pass filtering (to remove impulse noise), binarization, morphological processing (consisting of erosion and then closing operations), and thinning operation. The output images are shown in the figures 4 to 6 below. The morphological processing was done by referring to [9]. Fig.4: To the left is original image and the pre-processed image is to the right. Fig.5: To the left is the Skeleton of the fingerprint and the Region of Interest is to the right. Fig.6: To the left is Minutiae extraction and the image devoid of spurious Minutiae is to the right. Table 1 shows the comparison between the outputs of the usual, complete fingerprint verification and the partial fingerprint verification discussed in this paper. Table 1 Fig.3: Block diagram of Fingerprint Recognition 1 Ridge endings Algorithm using DWT 63 Sr. No. Property Image db11 (entire) Image db12 (partial)

5 2 Bifurcations Ridge Endings* Bifurcations* True Minutiae Per_match % 7 PF_mat 100 % 8 Reliability % Here * refers to the points after removal of false minutiae. Per_match describes the percentage of the partial image out of the entire image. In other words, in this case it denotes that the partial image is 61.11% of the entire image. PF_mat shows the percentage of the points (i.e. minutiae) in the partial image that matched with the entire image. The respective formulas for their computation are given below. A plot of Number of Minutiae in partial fingerprint versus Reliability shows a linear and direct proportionality as depicted in Fig.7. It is clear from the graph that as the number of minutiae in the partial fingerprint increase, the reliability approaches towards 100%. Thus, theoretically, the reliability would be 100% if the entire fingerprint is available for matching. Fig.7: Relation between reliability and minutiae points in partial fingerprint image. B. Observations: Following important observations were made while various SFIs from the aforesaid database were processed as a post acquisition step: As we increase the resolution of the input scanned image the quality of the output image is improved. By experimentation it was seen that the output image is the best when the input fingerprint is scanned at a resolution of 150 to 200 dpi (dots per inch). It should be noted that reconstruction of any part of the fingerprint cannot be done as it will give rise to the factor of probability of correctness and for fingerprint recognition we need to restrict ourselves to the real time available data. Preprocessing steps need not be exactly same for all fingerprint images. This is because each SFI may be taken in different physical conditions, that is, more ink present on the finger, ink not uniformly spread while giving impression for SFI and other similar cases. V. CONCLUSION The basic know-how for fingerprint recognition was studied. Accordingly a literature survey was done to learn about the current trends and the previous works in the field of aforesaid topic. Subsequently, a methodology was adopted based upon the literature study with certain modifications in the techniques. Thus, a joint scheme of minutiae matching and the wavelet transform based strategy is taken for further experimentation for the succeeding part of the project. Pre-processing of images was implemented / studied to understand the logic behind the algorithm and the effect on the various parameters of fingerprint image. Then the minutiae points were extracted from the partially acquired fingerprint image and the results were compared with complete fingerprint detection method. REFERENCES [1] Omid Zanganeh, B. Shrinivasan et. al. Partial fingerprint identification through correlation based approach. SCITEPRESS [2] National Science and Technology council Magazine. Fingerprint recognition. Pages [3] Mouad. M.H. Ali, V. Mahale, et.al. Fingerprint recognition for person identification and verification based on minutiae matching. International Conference on Advanced Computing [4] Maio, D.Maltoni, Capelli, Wayman,J.L., and Jain, A.K.(2006).Performance evaluation of fingerprint verification systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1):3 18. [5] Anil Jain, Fellow, Lin Hong, and Rud Bolle, Omline fingerprint verification, IEEE 64

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