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Fingerprint recognition is a widely used biometricidentification mechanism. In case of correlation based fingerprintrecognition detection of a consistent registration point is a crucialissue; this point can be a core point of a fingerprint. Manytechniques have been proposed but success rate is highlydependent on input used and accurate core point d...
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... case of the fingerprint which don't have core point we go for detection of point with high curvature regions or Low Coherence Strength. An example of Core point is shown in Fig.2. In [4] authors have described a fingerprint matching system based on Gabor filters, which uses circular tessellation around the core point and extracts Gabor Magnitude in 8 Directions, this is used as a feature vector, similar approach which uses a filter bank of Gabor filters is proposed in [5], they are using a set of filters to extract the fingerprint feature vectors and this is used for the training of classifier. ...
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Because automatic image analysis depends greatly on image segmentation, a new fast edge algorithm is proposed to trace ridge lines of the fingerprint images. Edge line skeleton method, based on edge point categorization, is also introduced. Fingerprint image core point is detected by an adaptive technique suggested in this work. The core is based o...
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Citations
... Since the imaging principle and acquisition approach of FP, FV, and FKP traits are different, diverse ROI extraction methods are supposed to be adopted accordingly [2]. In this paper, we apply the core point detection method to extract the FP ROI image [30], the convex direction coding method to extract the FKP ROI image [31], and the interphalangeal joint prior method to extract the FV ROI image [3]. Therefore, the FP, FV, and FKP images are cropped into 152 × 152 pixels, 200 × 91 pixels and 200 × 90 pixels, respectively. ...
Recently, finger-based biometrics, including fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP) with high convenience and user friendliness, have attracted much attention for personal identification. The features expression which is insensitive to illumination and pose variation are beneficial for finger trimodal recognition performance improvement. Therefore, exploring suitable method of reliable feature description is of great significance for developing finger-based biometric recognition system. In this paper, we first propose a correction approach for dealing with the pose inconsistency among the finger trimodal images, and then introduce a novel local coding-based feature expression method to further implement feature fusion of FP, FV, and FKP traits. First, for the coding scheme a bank of oriented Gabor filters is used for direction feature enhancement in finger images. Then, a generalized symmetric local graph structure (GSLGS) is developed to fully express the position and orientation relationships among neighborhood pixels. Experimental results on our own-built finger trimodal database show that the proposed coding-based approach achieves excellent performance in improving the matching accuracy and recognition efficiency.
... If core point exists, it is detected as registration point but for fingerprints having no core points, delta is the registration point. If neither core nor delta is found, a point with high curvature regions or Low Coherence strength is detected as the registration point [17]. For double core point fingerprint images, core point having the maximum distance from boundary is selected as the reference point. ...
Traditional fingerprint matching algorithms primarily focus on minutiae points on fingertip surface. In this paper, a novel approach is proposed for fingerprint matching that is based on ridge and valley characteristics of fingerprints. At first, the input fingerprint image is normalized and the registration point of that particular fingerprint is detected. Then a line profile is generated centering on that reference point. The distances between the reference point and ridges and the count of intersection points of line profile and ridges are stored in database. This process is repeated after every 15-degree angle to 345-degree in clock-wise direction and for orientation angle, the distances are stored sequentially. For matching intersection point count number along with the sequence of distance values are compared with the stored values. This new method can detect fingerprint from any orientation angle. Experimental result shows 90.87% accuracy of the proposed method.
... After the image is captured, it is sent to the data preprocessing module for preprocessing . To extract the ROIs of the three modality images, we, respectively, apply a method proposed in [7] for FKP ROI extraction, a method proposed in [20] for FP ROI extraction, and a method in [19] ...
Finger-based personal identification has become an active research topic in recent years because of its high user acceptance and convenience. How to reliably and effectively fuse the multimodal finger features together, however, has still been a challenging problem in practice. In this paper, viewing the finger trait as the combination of a fingerprint, finger vein, and finger-knuckle-print, a new multimodal finger feature recognition scheme is proposed based on granular computing. First, the ridge texture features of FP, FV, and FKP are extracted using Gabor Ordinal Measures (GOM). Second, combining the three-modal GOM feature maps in a color-based manner, we then constitute the original feature object set of a finger. To represent finger features effectively, they are granulated at three levels of feature granules (FGs) in a bottom-up manner based on spatial circular granulation. In order to test the performance of the multilevel FGs, a top-down matching method is proposed. Experimental results show that the proposed method achieves higher accuracy recognition rate in finger feature recognition.
... Masalah utama dalam merancang sistem klasifikasi sidik jari adalah menentukan fitur apa yang dipilih dan bagaimana fitur tersebut dapat digunakan untuk klasifikasi [2]. Klasifikasi sidik jari pada umumnya didasarkan pada ciri-ciri visual tertentu yang dimiliki oleh setiap sidik jari, misalnya keberadaan dan posisi core dan delta dan arah kemiringan ridge [3] [4]. Sidik jari dan fitur-fiturnya dapat dilihat pada Gambar 1. ...
Researches on fingerprint classification are generally based on its features such as core and delta. Extraction of these features are generally preceded by a variety of preprocessing. In this study the classification is done directly on the fingerprint image without preprocessing. Feature used as the basis for classification is the direction of the ridge. The direction of the ridge is determined by the slope of the blocks that are exist on every ridge. Fingerprint image is divided into blocks of size 3x3 pixels and the direction of each block is determined. Direction of the slope of the block are grouped into 8, these are north, north-east, east, south-east, south, south-west, west and north-west. The number of blocks in each direction form the basis of classification using Learning Vector Quantization network (LVQ). This study used 80 data samples from the database of FVC2004. This model obtained classification accuracy of up to 86.3%.
Keywords—fingerprint, classification, ridge, LVQ
... This method is efficient but very much sensitive to noise as the orientation deviation caused by image imperfection that effects the computation of PI. Another method is to detect the reference point by searching the curvature point from the orientation image [6][8] [3]. ...
Reference points play important role in the field of fingerprint recognition. It is mainly used for fingerprint classification
and fingerprint matching. There are many methods proposed for fingerprint reference point detection like Poincare Index technique,
Direction curvature technique etc. The purpose of this paper is to detect the reference points considering the uncertainty
for imperfection of fingerprint reference point position.
... Image-based methods include methods involving optical correlation [3] and transform-based features [2], [5], [6], [7]. Other aspects of fingerprint identification are orientation [8], segmentation [9] and core point detection [10]. They may achieve higher computational efficiency than minutiae based methods. ...
Using biometrics to verify a person’s identity has several advantages over the present practices of personal identification numbers (PINs) and passwords. Minutiae-based automated fingerprint identification systems are more popular, but they are more computationally complex and time consuming. In this paper we propose a simple yet effective technique for fingerprint identification. This method is image-based in which feature vectors of a fingerprint are extracted after sectorization of the cepstrum of a fingerprint. They are matched with those stored in the database. The experimental results show that this algorithm could correctly identify fingerprints with accuracy more than 96% in case of larger number of sectors.
Compared with unimodal biometrics systems, multimodal systems can effectively improve the recognition performance in accuracy and security. However, exploring a robust feature representation approach suitable for multiple modalities remains a crucial and challenging problem in the personal authentication community. In this paper, we proposed a discriminant local coding based convolutional neural network (LC-CNN) for multimodal finger recognition by fusing fingerprint, finger-vein, and finger-knuckle-print traits. Specifically, for each tri-modal finger images, we first presented a weighted local gradient coding (WLGC) operator to enhance the discriminant characteristics and formed the local coding layer (LC-layer) by reconstructing the WLGC operator with a bank of predefined convolution layers. Then, we established a simple yet effective LC-CNN model to extract deeper tri-modal finger features. Finally, the extracted feature vectors of three modalities are concatenated and input into a Support Vector Machine (SVM) for image classification. Experimental results demonstrate that the proposed method achieves a stable, highly accurate, and robust performance in multimodal finger recognition.
This paper presents an improved fingerprint recognition system for overlapping fingerprint of two fingers. The developed system can automatically and precisely separate an overlapping fingerprint into two areas, an overlapping area and a non-overlapping area, by analyzing their orientation fields and complicities. A method being able to remove a misjudging core of the fingerprint from the overlapping area is also proposed in this paper. Moreover, a recursive correction algorithm and a restrictions relaxation labeling algorithm are applied to separate two fingerprints from the determined overlapping fingerprint area, and the Gabor filter is applied to enhance figure quality of the two separated fingerprints. After that, the VeriFinger 6.2 SDK is used to identify the fingerprints. In the experiments, our system uses the fingerprints from the two public databases. Receiver Operating Characteristic (ROC) and Cumulative Match Characteristic (CMC), are used to examine the proposed fingerprint recognition system. The average recognition rate of our system is 85.7%. The experiments results show the effectiveness of the proposed system.
