Automatic Fingerprint Classification Scheme by Using Template Matching with New Set of Singular Point-Based Features

A robust singular point detection algorithm

Overcoming small inter-class variation when fingerprints have missing singular points (SPs) is one of the current challenges faced in fingerprint classification, since class information is scarce. Grouping the orientation fields to form Directional Patterns (DPs) shows potential in classifying these fingerprints. However, DPs change under rotation. This paper evaluates the change of DPs for fingerprints with missing SPs to determine a method of rotation that produces unique DPs for a Whorl (\( W \)) with a single loop and a single delta; a Right Loop (\( RL \)), Left Loop (\( LL \)), Tented Arch (TA) and a \( W \) with a single loop; a \( RL \) and \( LL \) with a single delta; and lastly a Plain Arch (\( PA \)) and a Partial Fingerprint (\( PF \)) with no SPs. The proposed method of rotation is based on the remaining SPs and achieves a manual classification accuracy of 91.72% on the FVC 2002 and 2004 DB1, and FVC 2004 DB2.

Latent fingerprints left at crime scenes are useful evidence in the court of law. Law enforcement agencies have been using latent fingerprints for many years as a reliable forensic evidence for crime scene analysis. However, due to poor quality images and complex image background, current state-of-the-art for automatic latent fingerprint processing is not as reliable as rolled or live scan fingerprints. In this paper, we propose a Convolutional Neural Network (CNN) based model (LAFIN) to classify latent fingerprints into five different classes. The classification result along with fingerprint image is then fed to a mini classifier to extract the singular points (if any) present in the latent fingerprint. The CNN model for latent fingerprint classification is trained on different fingerprint images from IIIT-D latent database and NIST Special database 4. The results on publicly available IIIT-D latent fingerprint database demonstrate the efficiency of the proposed approach.

As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.

AFIS (automated fingerprint identification system) is very popular now days for biometric security. Fingerprint classification plays a key role in identifying fingerprints and also helps in fingerprint matching. This paper presents a new classification technique based on the detection of singular points (core and delta points). This fingerprint classification technique consists of four steps. In the first step, preprocessing (segmentation and normalization) of input fingerprint image is done. In the second step, fine orientation field of image is estimated. In the third step, singular points are located using modified Poincare index technique. In the fourth step, classification is done on the basis of singular points. The proposed technique was tested on FVC2004 database and the results show a significant improvement in reducing the misclassification errors.

Fingerprint classification

Fingerprint analysis is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. A core-delta relation is used as a global constraint for the final selection of singular points. This paper presents and discusses rules for implementing a structural fingerprint classifier. The proposed approach is divided in two main phases: Singular points extraction and recognition algorithm. The fingerprint classification is heavily depended on the singularities, i.e., core point and delta points. For the recognition of fingerprint a new technique is proposed based on the number of singular points and their types.

We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs), and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features which can be integrated in the SVMs. SVMs are combined with a new error correcting code scheme which, unlike previous systems, can also exploit information contained in ambiguous fingerprint images. Experimental results indicate the benefit of integrating global and structured representations and suggest that SVMs are a promising approach for fingerprint classification.

An algorithm is proposed, which combines global and local information of fingerprint images to detect singular points. It’s mathematically proven that normal lines of gradient of double orientation field(GDOF) pass through singular points. Normal lines of GDOF use rather global information to detect candidate singular points. Fingerprint image is divided into blocks and normal lines of GDOF are drawn. The number of normal lines that pass through each block is accumulated. The block that has the maximum number corresponds to a candidate singular point. It can be seen as a kind of Hough transform(HT). As candidate singular points detected by global information may have a little warp from their real positions, it’s necessary to use local information to refine their positions. Poincare index is chosen, and uses local information to refine the candidate singular points. This makes our algorithm more robust to noise than methods that only use local information. What’s more, the pairs of the detected singular points are used to classify fingerprint. Experimental results show that our algorithm performs well and fast enough for real time application in databases NIST-4.

Fingerprints classification using artificial neural networks: a combined structural and statistical approach

Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines

Fingerprint is the oldest techniques for biometric identification. This limb has its own characteristics for each individual to distinguish from each other. Fingerprint is unique and the pattern will never change. Many approaches have been developed in this field research. Bag-of-Visual Words (BoVW) is an approach for fingerprint classification, where the classification is based on the histogram of the frequency of visual words. The problem arises when the fingerprint classification uses the original images in which these images have a large size and in large numbers. It requires large storage space and high computational time. In order to overcome this problem, we propose Singular Value Decomposition (SVD) as compression method that can reduce the storage space. Our proposed method has been evaluated with some experiments based on five different types of fingerprint (whorl, arch, left loop, right loop, and twin loop). Experimental results show that the proposed method reduces the storage space and computational time. Moreover the accuracy of fingerprint classification could be improved.

BackgroundOrientation field (OF) plays a very significant role in automatic fingerprint recognition systems. Many algorithms have been proposed for the estimation of fingerprints’ OF but it is hard to solve the dilemma of correcting spurious ridge structure and avoiding singularity location deviation, especially for poor images. So far, the following drawbacks still need to be solved for OF construction methods for practical application: (1) How to adaptively choose block scales to resolve the contradiction between accuracy and anti-noise, since small scale is beneficial to accuracy but is sensitive to noise, while large scale is more resistant to noise, but the accuracy is deteriorated. (2) How to construct the genuine OF in the areas close-by singular points and to evade singularity location deviation? Current block based methods give spurious OF estimates in the area near singular points because these areas have large curvature thus the detected singular points deviate from the genuine localizations. When these singular points are used as the anchor for referencing minutiae, it makes the average error of matching or recognition even larger. Therefore, it is essentials to construct the genuine OF in the areas close-by singular points and to evade singularity deviation.MethodsTo overcome the above-mentioned limitations, a novel method, combining a weighted multi-scale composite window (WMCM) with a hierarchical smoothing strategy has been proposed for the computation of fingerprint OF. This method mainly contains two procedures: the approximate OF estimation and the hierarchical OF smoothing. In the first procedure, a series of OFs are established under multiple scales of composite windows by using a gradient based method then a coarse OF is estimated using the weight of each scale determined by a squared gradient consistency. In the second procedure, the OF is first quantized into a two-digitized orientation zone and a two-orientation-zone filtering strategy is adapted to the OF blocks based on a filtering mask obtained after eliminating the isolated blocks. In the end a similar three-digitized orientation zone is performed to obtain an accurate and smooth OF. To validate the performance, the proposed method has been applied to OF computation using the FVC2004 databases and three experiments are designed. Experiment 1 aims to validate whether the weighted multi-scale composite window can balance the dilemma of accuracy and robustness more effectively than the previous works do. Experiment 2 is designed to examine whether the hierarchical smoothing method can correct the spurious ridge flow and preserve the genuine localization of singular points. The purpose of experiment 3 is to test the performance of the proposed method on OF reconstruction in low quality fingerprint images. The fingerprint databases FVC 2004 DB1–DB4 are employed in this study.ResultsThe results of experiment I shows that the proposed method is capable to extract the information of OF reliably and it is more robust against singularity localization deviation in comparison with the other three gradient based methods. The results of experiment II indicates that the proposed smoothing method can balance the contradiction in correcting spurious ridge structures and preserving genuine singularity localization. The results of experiment III illustrates that our approach combing WMCW with the hierarchical smoothing method is capable to extract the information of OF ridge reliably and it is more robust against singularity deviation in comparison with the other three gradient based methods. In a word, the experiment results demonstrate that the proposed method can correct spurious ridge structure and meanwhile avoid singularity deviation compared with the previous works.ConclusionsA novel gradient based algorithm has been proposed which is more reliable for the estimation of the ridge information for fingerprint OF and is more accurate in preserving the singularity localization. Compared with the previously proposed gradient based methods, the advantages of the proposed RBSF lie in three aspects. Firstly a weighted multi-scale composite window is put forward to replace the single window used by conventional gradient based methods and to adaptively choose the scales of the blocks. Secondly, a hierarchical smoothing strategy is proposed to enhance the OF by using the two-orientation-zone filtering and the three-orientation-zone filtering, aiming to correct the spurious ridges and preserving the genuine location of singular points. Finally, three experiments are designed to test the proposed algorithm together with other popular gradient based methods on real fingerprint images, which are selected from different categories and all are suffering from obvious noise effects. All the experiment results show that the proposed method is superior with respect to reliable OF construction and avoiding singularity localization deviation.

The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint images is a very complex pattern recognition problem, due to properties of intra-class diversitiesand inter-class similarities. Its objective is to reduce the responsetime and reducing the search space in an automatic identificationsystem fingerprint (AIS), in classifying fingerprints. In these papers we present a system of fingerprint classificationbased on singular characteristics for extracting feature vectorsand neural networks and fuzzy neural networks, SVM and Knearest neighbour for classifying.

Fingerprint classification is crucial to reduce the processing time in a large-scale database. In this paper a fingerprint classification based on continuous orientation field and singular points is proposed. The continuous orientation field can not only filter the noises in point directional image, but also represent the basic structural feature of fingerprint more precisely. Singularities are the most important and reliable feature in classification. The reliable and fast classification algorithm is made possible by a simple but effective combination of continuous orientation field and the modified Poincare index in the determination of singular points.The experiment results show the effectiveness of the proposed method in producing good classification result.