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The matching process. The minutiae matching module provides the transformation parameters necessary to align the query image with the template.
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![Fig. 10. Flowchart of the minutiae extraction algorithm [7].](profile/Arun-Ross-2/publication/224725987/figure/fig5/AS:393685570211844@1470873362409/Flowchart-of-the-minutiae-extraction-algorithm-7_Q320.jpg)
We describe a hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints. A set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A square tessellation of the...
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Fingerprint, as one of the most popular and robust biometric traits, can be used in automatic identification and verification systems to identify individuals. Fingerprint matching is a vital and challenging issue in fingerprint recognition systems. Most fingerprint matching algorithms are minutiae-based. The minutiae in fingerprints can be determin...
Citations
... Paulino et al. [34] presented a method to fuse manually marked and automatically extracted minutiae templates to improve latent fingerprint recognition performance. Several studies [5,33,35] have investigated the fusion of plain and rolled fingerprints in latent finger recognition task. However, these fusion methods had limited performance improvements in terms of accuracy because of distortion and artifacts within latent images. ...
Latent fingerprints are ubiquitously used as forensic evidence by law enforcement agencies in solving crimes. However, due to deformations and artifacts within latent fingerprint images, performance of the automated latent recognition systems are far from desired levels. A basic matcher specifically designed for clean fingerprints using a minutiae-based matching algorithm can have high speed and accuracy in a sensor-to-sensor matching task, but low accuracy in matching latent prints, due to scale, rotation and quality differences between latent and sensor images. In this study, we propose a unique multistep fusion matcher (FM) on top of a base matcher that would utilize scale, rotation, and quality attributes of minutiae with speed, memory, and accuracy trade options in the latent recognition process. FM match characteristics are analyzed by using a private dataset consisting of 5560 latent and 1M slap/rolled fingerprint images. In addition, 292 domain expert selected latents are used to compare the nationwide performance of the proposed method. FM’s with multiresolution fusion (MRF) option have achieved competitive accuracy rates when searching 292 latent against 1 million background and projecting predictions for 69 million background. On the NIST SD302 public dataset, FM6 (FM option prioritizing accuracy for latent-to-sensor search) with MRF correctly recognizes 911 latent in rank-1, while the COTS system referenced in the NIST SD302 documentation recognizes only 790 from a gallery composed of 5950 latent and 100K rolled background database. FM6 MRF rank-1 count for 10K latent of NIST SD302 is 1415, whereas NIST’s referenced matcher rank-1 count is 880 for the same dataset. In addition, NIST SD302 rank-1 latents are used to construct 722 latent pairs to evaluate latent-to-latent matching performance. FM8 (FM option prioritizing accuracy for latent-to-latent search) with MRF has 46.1% rank-1 identification rate for latent-to-latent search against 10K latent background. Moreover, on a private 1457 latent palmprint versus 2296 sensor palmprint background, a palm matcher designed by dividing latent and palm images into 512x512 pixel segments produces 85.45% rank-1 accuracy by using FM6.
... Texture features, such as local binary patterns (LBPs), histograms of oriented gradients (HoGs), and Gabor responses [6][7][8][9][10] are useful descriptors for a fingerprint-matching system; however, the texture of fingerprints varies because of the differences among sensors. Figure 1 shows zoomed-in views of some fingerprints of the same finger but captured with different sensors; the corresponding LBP images are shown in Figure 2. The LBP features differ from one another, showing large inter-class variations; there is concern regarding the ability of texture descriptors to discriminate the fingerprints captured with different sensors. ...
The fingerprint is a commonly used biometric modality that is widely employed for authentication by law enforcement agencies and commercial applications. The designs of existing fingerprint matching methods are based on the hypothesis that the same sensor is used to capture fingerprints during enrollment and verification. Advances in fingerprint sensor technology have raised the question about the usability of current methods when different sensors are employed for enrollment and verification; this is a fingerprint sensor interoperability problem. To provide insight into this problem and assess the status of state-of-the-art matching methods to tackle this problem, we first analyze the characteristics of fingerprints captured with different sensors, which makes cross-sensor matching a challenging problem. We demonstrate the importance of fingerprint enhancement methods for cross-sensor matching. Finally, we conduct a comparative study of state-of-the-art fingerprint recognition methods and provide insight into their abilities to address this problem. We performed experiments using a public database (FingerPass) that contains nine datasets captured with different sensors. We analyzed the effects of different sensors and found that cross-sensor matching performance deteriorates when different sensors are used for enrollment and verification. In view of our analysis, we propose future research directions for this problem.
... Multi-algorithm systems process same biometric data with different algorithms. E.g texture and minutiae based algorithm process the same fingerprint image for extractionvarious features for improvement in performance of authentication / authorization system [5]. Salima Nebti implemented face recognition system with two different classifiers [6]. ...
Biometrics is becoming necessity for security issues in Information Technology trend. Authentication and authorization purposes can be served efficiently and effectively with biometric techniques. But sometimes unimodal biometric systems (systems using one biometric trait) may not serve purpose because of various reasons. Mutibiometric systems resolve the issues which cannot be addressed using unimodal biometric systems. The author has presented comparison of various multibiometric systems in this paper.
... Minutiabased techniques [5], [6], [7], [8], [9], on the other hand, attempt to align two sets of minutiae points and determine the total number of matched minutia. Hybrid methods [10], [11], [12] employ both minutia and non-minutia (e.g. ridges) features for matching. ...
A novel minutia-based fingerprint matching algorithm is proposed that employs iterative global alignment on two minutia sets. The matcher considers all possible minutia pairings and iteratively aligns the two sets until the number of minutia pairs does not exceed the maximum number of allowable one-to-one pairings. The optimal alignment parameters are derived analytically via linear least squares. The first alignment establishes a region of overlap between the two minutia sets, which is then (iteratively) refined by each successive alignment. After each alignment, minutia pairs that exhibit weak correspondence are discarded. The process is repeated until the number of remaining pairs no longer exceeds the maximum number of allowable one-to-one pairings. The proposed algorithm is tested on both the FVC2000 and FVC2002 databases, and the results indicate that the proposed matcher is both effective and efficient for fingerprint authentication; it is fast and does not utilize any computationally expensive mathematical functions (e.g. trigonometric, exponential). In addition to the proposed matcher, another contribution of the paper is the analytical derivation of the least squares solution for the optimal alignment parameters for two point-sets lacking exact correspondence.
... The noise and other distortion during the acquisition of the fingerprint and errors in the minutia extraction process can result in spurious and missing minutiae that easily degrade the performance of the recognition [4]. Another problem is that the rotation and displacement of the finger placed on the sensor, can lead to different images for the same fingerprint as and that what we want to focus on in this paper and as it shown in Fig.1such that they have only a partial overlap area resulting in only a small number of corresponding minutiae points [5]. ...
... Arun Ross et. al., [16] proposed the hybrid fingerprint matcher which employs the combination of ridge strengths and a set of minutiae points. Johg Ku Kum et. ...
... Volume XVI Issue II Version I 16 Year 2016 ( ) [76] 1996 Hough transform-based approaches ----------- [77] 1997 Ridge-based relative pre-alignment ----------- [67] 2004 Minutiae matching THU [78] 2005 Global matching of clusters of minutiae ----------- [68] 2006 Invariant moment finger Code and LVQ FVC2002 [80] 2006 Global minutiae matching with image correlation ---------- [69] 2007 Minutiae matching, vector matching ,weight modification and local area matching process FVC2002 [70] 2008 ...
... Yao et al. [30] proposed Gabor transformation to extract the texture of palm print features which divided the palm print image into 32 regions. And it was used eight direction (0, /8, /4,3 /8, /2,5 /8,3 /4,7 /8) and four scales (2,4,8,16) 8*4=32 regions to obtain the image texture characteristics. Then it was resized the domination of Gabor image into 1/16 of original image. ...
This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolment & recognition the biometrics data. Secondly, the pre-processing stage which includes the enhancement and segmentation of Region-Of-Interest ROI. Thirdly, features extracted from the output of the preprocessing and each modal of biometrics having different type of features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. Multimodal is being gathered of various types of biometrics objects from the same human. In this paper, the biometric system gives an explanation for each model. Also, the modalities of biometrics are discussed as well as focused on two different modalities: fingerprint and Palm-Print.
... Arun Ross et. al., [16] proposed the hybrid fingerprint matcher which employs the combination of ridge strengths and a set of minutiae points. ...
This article is an overview of a current research based on fingerprint recognition system. In this paper we highlighted on the previous studies of fingerprint recognition system. This paper is a brief review in the conceptual and structure of fingerprint recognition. The basic fingerprint recognition system consists of four stages: firstly, the sensor which is used for enrolment & recognition to capture the biometric data. Secondly, the pre-processing stage which is used to remove unwanted data and increase the clarity of ridge structure by using enhancement technique. Thirdly, feature extraction stage which take the input from the output of the pre-processing stage to extract the fingerprint features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. The aim of this paper is to review various recently work on fingerprint recognition system and explain fingerprint recognition stages step by step and give summaries of fingerprint databases with characteristics.
... Biometric is the science of identifying and verifying the identity of a person based on physiological or behavioral characteristic (Jain et al., 2004; Al-Hamdani et al., 2013). Biometric security system are widely used which mostly include fingerprint (Ross et al., 2003), face recognition (Park et al., 2005), iris (Ibrahim, 2014), speech recognition (Plannerer, 2005) and etc. Because of the complex structure of capillaries which supply the retina with blood, each person's retina and also person's eye is unique and has unchanging nature. ...
Biometric security has become more important because of the Increasing activities of hackers and terrorists. Retinal biometric system is one of the most reliable and stable biometrics for the identification/verification of individuals in high security area rather than other biometric. Also no two people have the same retinal pattern and then cannot be stolen or forget. Due to these reasons this study presents a system for individual recognition based on vascular retina pattern. This approach is robust to brightness variations, noise and it is insensitive to rotation. The proposed method consists of three main stages (i.e., preprocessing, feature extraction and finally matching stage). Preprocessing is done to make the required color band separation, remove the rotation appearance which might occur during the scanning process and modify the image brightness to simplify the process of extracting vascular pattern (region of interesting) from input retina (i.e., feature vector) in the feature extraction stage. Finally, the discrimination process of features is evaluated and the results utilized in matching stage. The proposed method is tested on the two publicly available datasets: (i) DRIVE (Digital Retinal Images for Vessel Extraction) and (ii) STARE (Structured Analysis of the Retina). The achieved accuracy of recognition rate was equal to 100% for all datasets.
... A different type of information fusion in biometrics involves the combination of the data coming from a single biometric modality by applying multi-algorithmic fusion techniques. In the field of fingerprint biometrics there are several examples of information fusion implemented using multiple algorithms in different stages of the recognition process [13][14][15]. The work presented by Vatsa et al. [16] employed the iris as the single modality for implementing multialgorithmic information fusion. ...
This paper presents a research for the use of multi-source information fusion in the field of eye movement biometrics. In the current state-of-the-art, there are different techniques developed to extract the physical and the behavioral biometric characteristics of the eye movements. In this work, we explore the effects from the multi-source fusion of the heterogeneous information extracted by different biometric algorithms under the presence of diverse visual stimuli. We propose a two-stage fusion approach with the employment of stimulus-specific and algorithm-specific weights for fusing the information from different matchers based on their identification efficacy. The experimental evaluation performed on a large database of 320 subjects reveals a considerable improvement in biometric recognition accuracy, with minimal equal error rate (EER) of 5.8%, and best case Rank-1 identification rate (Rank-1 IR) of 88.6%. It should be also emphasized that although the concept of multi-stimulus fusion is currently evaluated specifically for the eye movement biometrics, it can be adopted by other biometric modalities too, in cases when an exogenous stimulus affects the extraction of the biometric features.
... The practical applications of fingerprints recognition and characterization ranges from public or private safety, to forensic medicine, legal matters, among other applications, a review of it with a statistical analysis was done by Abraham et al. as solved [5]. In fact, the recognition and classification of fingerprints, either from their minutae or their mutual mathematical correlation, represents a very active area of R&D nowadays and a number of methodologies have been proposed, including hybrid matchers, e.g., as solved by Ross et al. as solved [7] and wavelet transforms, e.g., as solved by Nanni et al. as solved [8]. From the Forensic Science point of view, there exist two main methods for fingerprints identification, based on a standard, and somewhat arbitrary, classification. ...
A pc-based automatic system for fingerprints recording and classification is described, based on the vector analysis of bifurcations. The system consists of a six-step process: a) acquisition, b) preprocessing, c) fragmentation, d) representation, e) description, and f) recognition. Details of each stage, along with actual examples of fingerprints recognition are provided.
