Palmprint Recognition

Palmprint is one of the main biometric features that has gained a lot of attention recently due to its commercial applications. Palmprint is more flexible in identifying an individual based on wrinkles, ridges and Principal lines. These features do not change during the lifetime of a person and no two individuals have similar palmprints. Most of the conventional methods use edge detection operators to detect lines in the palm which produce too many trivial lines. In this paper, a novel technique has been proposed to extract principal lines in two phases without using edge detection. The first phase involves some preprocessing operations and the second phase consists of morphological operations and elimination of undesirable components. The experimental results show that the proposed technique is more effective than existing techniques.

One of the most popular and legally recognized behavioral biometrics is the individual's signature, which is used for verification and identification in many different industries, including business, law, and finance. The purpose of the signature verification method is to distinguish genuine from forged signatures, a task complicated by cultural and personal variances. Analysis, comparison, and evaluation of handwriting features are performed in forensic handwriting analysis to establish whether or not the writing was produced by a known writer. In contrast to other languages, Arabic makes use of diacritics, ligatures, and overlaps that are unique to it. Due to the absence of dynamic information in the writing of Arabic signatures, it will be more difficult to attain greater verification accuracy. On the other hand, the characteristics of Arabic signatures are not very clear and are subject to a great deal of variation (features’ uncertainty). To address this issue, the suggested work offers a novel method of verifying offline Arabic signatures that employs two layers of verification, as opposed to the one level employed by prior attempts or the many classifiers based on statistical learning theory. A static set of signature features is used for layer one verification. The output of a neutrosophic logic module is used for layer two verification, with the accuracy depending on the signature characteristics used in the training dataset and on three membership functions that are unique to each signer based on the degree of truthiness, indeterminacy, and falsity of the signature features. The three memberships of the neutrosophic set are more expressive for decision-making than those of the fuzzy sets. The purpose of the developed model is to account for several kinds of uncertainty in describing Arabic signatures, including ambiguity, inconsistency, redundancy, and incompleteness. The experimental results show that the verification system works as intended and can successfully reduce the FAR and FRR.

Bu çalışmada, örüntü tanımanın en önemli kısmı olan özellik çıkarma için geliştirilmiş etkili bir yöntem sunulmuştur. Darbeli radarlarda hedef sınıflama için AR modelinin güç spektrumu ve yapay sinir ağı temelli, yeni bir özellik çıkarma yöntemi geliştirilmiştir. Bu yöntemle ölçülen darbeli radar işaretlerinin AR modeli güç spektrumundan elde edilen 512 adet özellik kullanılarak sınıflama yapılmaktadır. Özelliklerin frekans bölgesinden elde edilmesi ve özellik sayısının fazla oluşu yöntemin güvenirliğini ve etkinliğini yükseltmektedir. Sınıflama yapısı ileri beslemeli ve geri yayınım öğrenme algoritmalı yapay sinir ağı üzerine kurulmuştur. Böylelikle akıllı ve otomatik bir sınıflama gerçekleşmesi sağlanmıştır.

Chaos game mechanism is a procedure for creating fractal phenomena from another fractal using a polygon and random walk. Here, the authors propose a novel identification approach and efficient application of Chaos Game theory towards signature identification. In fact, a signature is produced with hand scripting with a special rhythm that belongs to a specific person. Although a signature is a behavioural feature among the human biometric features, this behavioural feature expresses a chaotic property and can be analysed with chaotic systems and fractal theory. With authors' technique, by using a Chaos Game theory, a new fractal is created for each signature instance and, during the creation of the fractal, new features are extracted. These features express the fractal properties of the signature and are unique. In addition, by using fractal theory, this technique benefits from the advantages of fractal phenomena such as stability against rotation, losing some parts of the signature and scale that is desirable for biometrics applications. Authors' approach for offline signature analysis can segregate and identify many signature instances with a desirable time complexity. The authors name the technique that the authors present here the chaos game signature identification (CGSI).

, where she also codirects the Artefact Lab and the Bricolab. Her research is based in media studies and aesthetics, and the history of technology. She is currently preparing High-Tech Paper: Security Printing and the Aesthetics of Trust, a monograph that examines the making of authentic paper for circulation in secure systems and infrastructures. She situates security printing within media and printing histories, but also as it intersects with art, craft, and design. Her work on authentication devices includes the production of Nano-verses (nano-verses.com), an art-sci collaboration that explored how the technology of nano-optical authentication can be rethought as artistic media. The articles discussed in the following interview are: "Storing Authenticity at the Surface and into the Depths: Securing Paper with Human-and Machine-Readable Devices" (Intermédialités, 2018); "'Don't Copy That': Security Printing and the Making of High-Tech Paper" (Convergence, 2019); and "The Intrinsic Value of Valuable Paper: On the Infrastructural Work of Authentication Devices" (Theory, Culture & Society, 2020). She recently co-edited an issue of PUBLIC: Art/Culture/Ideas on "Biometrics: Mediating Bodies" (2020), and is currently finalizing a co-edited volume of the Canadian Journal of Communication on the theme of "Materials and Media of Infrastructure."

Encounter-type haptic interfaces are used to interact physically with virtual environments. They allow controlling the position of an avatar in the simulation while perceiving the forces applied on it when it interacts with the surrounding objects. Contrary to usual force feedback devices, the interface tracks the real user's finger without touching it when the user's finger avatar moves in free space. Only when a contact occurs in the virtual environment, the interface comes in contact with the user to display the mechanical properties of the encountered objects. This way, the device's behaviour is more natural as simulated contacts really occur in the real world. Existing control laws for such devices exhibit however limitations, especially when contacts occur at high speed. In such cases, the device tends to bounce against the user's finger, which decreases the realism of the interaction. In this paper, we propose a new control strategy where the interface is first stabilized against the obstacles before the user touches its end-effector. This way, contacts appear more natural, even at high speeds, as confirmed by preliminary user-tests made with an existing 2 DoF encounter type haptic interface at different speeds with the state of the art control law and the novel approach we propose here.

Haptic interfaces are proposed for enhancing human-computer interfaces through the rendering of interaction forces. Among them, encounter-type haptic displays feature an original way to provide the user with an improved transparency: their end effectors remotely track the fingers of the operator and encounter them only when force rendering is required. These interfaces call for the development of specific position tracking systems. In this paper, a 2-D hollow instrumentation for remote and marker-free close-range finger tracking is proposed. It encloses 16 infrared proximity sensors. Its design specifications are based on a literature review of finger motion and a simulation of the tracking dynamics. Some calibration methods, organized as a function of their complexity level, are also described. The instrumentation performances are experimentally evaluated to assess its adequacy with the targeted application. This design accounts for the demonstration that infrared proximity sensing is a viable solution for close-range sensing of targets as changing as human body parts.

This paper introduced some new techniques to extract the palmprint features based on fractal codes and fractal dimension. Four new techniques based on fractal, namely FDFC (fractal dimension from fractal codes), FDIFC (fractal dimension image from fractal code), DIFC (distance image from fractal code), and AIFC (Angle image from fractal code) are proposed.. The main idea of these methods is the forming palmprint image representation by using fractal code parameters. There are three parameters of fractal codes are used namely position of range block, position of domain block, and size of range block. Those methods have been applied for palmprint verification system. The system has been tested by using database of 1050 palmprint images, are generated from 5 samples from each of the 210 persons randomly selected. Experiment results show that our proposed methods can achieve an acceptable accuracy rate.

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases,

Breast cancer is the most common malignancy of women and is the second most common and leading cause of cancer deaths among them. At present, there are no effective ways to prevent breast cancer, because its cause is not yet fully known. Early detection is an effective way to diagnose and manage breast cancer can give a better chance of full recovery. Therefore, early detection of breast cancer can play an important role in reducing the associated morbidity and mortality rates. In this paper, using contourlet transform that can capture the intrinsic geometrical structure that is key in visual information. The contourlet expansion is composed of basis images oriented in various directions in multiple scales, with flexible aspect ratios. The basic idea of this thesis is to design and implement a proposed system that can aid the physician in reading a mammogram image by study the usage of wavelet and contourlet transform based on various operations on mammogram images and classifies them as normal, benign or malignant based on the decision tree ID3 algorithm. The experimental results show that the ID3 classifier achieves accuracy of 81% in the case of wavelet transform and 95% with contourlet transform for the same number of the test set.

Recently, a considerable amount of research has been focused on inverse biometrics, that is, regenerating the original biometric sample from its template. In this work, the first reconstruction approach to recover hand geometry samples from their feature vectors is proposed. Experiments are carried out on the publicly available GPDS Hand DB, where the method has shown a remarkable performance, after reconstructing a very high percentage of the hands included in the dataset. Furthermore, the proposed technique is general, being able to successfully reproduce the original hand shape sample regardless of the information and format of the template used.

I. INTRODUCTION HIS paper investigates the application of face recognition biometrics in travel documents for validation, verification and prevention with regards to international security. Post 9/11 incident, the United States of America (US) passed the PATRIOT Act and the ...

This paper introduced some new techniques to extract the palmprint features based on fractal codes and fractal dimension. Four new techniques based on fractal, namely FDFC (fractal dimension from fractal codes), FDIFC (fractal dimension image from fractal code), DIFC (distance image from fractal code), and AIFC (Angle image from fractal code) are proposed.. The main idea of these methods is the forming palmprint image representation by using fractal code parameters. There are three parameters of fractal codes are used namely position of range block, position of domain block, and size of range block. Those methods have been applied for palmprint verification system. The system has been tested by using database of 1050 palmprint images, are generated from 5 samples from each of the 210 persons randomly selected. Experiment results show that our proposed methods can achieve an acceptable accuracy rate.

Forensic palmprint recognition, which mainly deals with high-resolution palmprints and latent-to-full palmprint comparison, has aroused research highlights because of the increased use of the evidence of palmprints in forensics. There are some in-depth works on high-resolution palmprint preprocessing (i.e., segmentation and enhancement) and feature extraction. However, few works on latent-to-full palmprint comparison have been done. Recently, radial triangulation-based latent-to-full palmprint comparison algorithm was proposed as it has been proposed for forensic likelihood ratio computation using fingerprints, and proved to have identification usability and efficiency for palmprint comparison. In this work, we generalize point pattern comparison by relaxation to minutiae-based palmprint recognition and improve the latent-to-full palmprint comparison algorithm based on radial triangulation. Firstly, local minutiae comparison is modified according to novel point pattern comparison method and global minutiae comparison is based on centroid distribution. Then logistic regression learning is used for comparison score computation. Performance of the proposed algorithm is evaluated on forensic databases including 22 latent palmprints from real cases and 8680 full palmprints from criminal investigation field. Experimental results show the improvement on identification accuracy and efficiency of our approach. A rank-1 identification rate of 69% is achieved, compared with 63% of previous radial triangulation-based.

Recently, a novel matching strategy based on regional fusion for high resolution palmprint recognition arises for both forensic and civil applications, under the concept of different regional discriminability of three palm regions, i.e., interdigital, hypothenar and thenar. This matching strategy requires accurate automatic region segmentation techniques since manual region segmentation is time consuming. In this work, we develop automatic region segmentation techniques based on datum point detection for high-resolution palmprint recognition which can be further applied to forensic applications. Firstly, Canny edge detector is applied to a full palmprint to obtain gradient magnitudes and strong edges. Then a first datum point, i.e., the endpoint of heart line, is detected by using convex hull on gradient magnitude image and its left/right differential image and strong edge image. A second datum point, i.e., the endpoint of life line, is estimated based on the position and direction of the first datum point and statistical average distance between the two datum points. Finally, segmented palm regions are generated based on the two datum points and their perpendicular bisector. To evaluate the accuracy of our region segmentation method, we compare the automatic segmentation with manual segmentation performed on a public high resolution palmprint database THUPALMLAB with full palmprint images. The regional error rates of interdigital, thenar and hypothenar regions are 15.72%, 17.05% and 21.38% respectively. And the total error rate is 19.54% relative to full palmprint images.

Recently, a novel matching strategy based on regional fusion for high resolution palmprint recognition arises for both forensic and civil applications, under the concept of different regional discriminability of three palm regions, i.e., interdigital, hypothenar and thenar. This matching strategy requires accurate automatic region segmentation techniques since manual region segmentation is time consuming. In this work, we develop automatic region segmentation techniques based on datum point detection for high-resolution palmprint recognition which can be further applied to forensic applications. Firstly, Canny edge detector is applied to a full palmprint to obtain gradient magnitudes and strong edges. Then a first datum point, i.e., the endpoint of heart line, is detected by using convex hull on gradient magnitude image and its left/right differential image and strong edge image. A second datum point, i.e., the endpoint of life line, is estimated based on the position and direction of the first datum point and statistical average distance between the two datum points. Finally, segmented palm regions are generated based on the two datum points and their perpendicular bisector. To evaluate the accuracy of our region segmentation method, we compare the automatic segmentation with manual segmentation performed on a public high resolution palmprint database THUPALMLAB with full palmprint images. The regional error rates of interdigital, thenar and hypothenar regions are 15.72%, 17.05% and 21.38% respectively. And the total error rate is 19.54% relative to full palmprint images.

Efficient eGovernment and eCommerce require the ability to authenticate citizens and transactions online, whereas the increasing mobility of citizens demands reliable identification. Identity documents tend to become the most popular form of identity tokens used for these purposes. An important problem, however, is that they can easily be passed on or used by a fraudster. We discuss the use of identity documents and the problem of linking these documents with their genuine holder. We discuss ePassports and eID cards in general using the Belgian identity documents as a reference.

Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, va...

This article proposes robust features fusion methodology for supervised palmprint recognition. The process of features fusion has been formulated according to the hybridization between robust morphological features of principal lines and the following features extractors: principal component analysis (PCA), transformed domain extractors, and invariant moments. The region of interest (ROI) has been extracted based on the identification of the valley points locations. Moreover, both morphological operations and edge detection have been applied in order to formulate the correct ROI. First, both length and slope of each extracted principal line have been estimated morphologically. Second, different features extraction techniques have been applied for the resultant ROI including (PCA), transformed domain, and invariant moments. Finally, seven supervised machine learning methods have been manipulated in order to achieve the highest palmprint recognition accuracy. The accuracy of the recognition was evaluated by measuring sensitivity, dice, precision, Jaccard coefficients, correlation, accuracy, and recognition time. Experimental findings show that, among all tested machine learning methods, the feed forward neural network with back propagation based on features vector of the fusion between invariant moments and principal lines morphology has achieved approximately 99.9% of successful palmprints recognition. K E Y W O R D S discrete cosine transform, discrete wavelet transform, invariant moments, principal component analysis, Hough transform, morphological features, transformed domain.

Recently, a novel matching strategy based on regional fusion for high resolution palmprint recognition arises for both forensic and civil applications, under the concept of different regional discriminability of three palm regions, i.e., interdigital, hypothenar and thenar. This matching strategy requires accurate automatic region segmentation techniques since manual region segmentation is time consuming. In this work, we develop automatic region segmentation techniques based on datum point detection for high-resolution palmprint recognition which can be further applied to forensic applications. Firstly, Canny edge detector is applied to a full palmprint to obtain gradient magnitudes and strong edges. Then a first datum point, i.e., the endpoint of heart line, is detected by using convex hull on gradient magnitude image and its left/right differential image and strong edge image. A second datum point, i.e., the endpoint of life line, is estimated based on the position and direction of the first datum point and statistical average distance between the two datum points. Finally, segmented palm regions are generated based on the two datum points and their perpendicular bisector. To evaluate the accuracy of our region segmentation method, we compare the automatic segmentation with manual segmentation performed on a public high resolution palmprint database THUPALMLAB with full palmprint images. The regional error rates of interdigital, thenar and hypothenar regions are 15.72%, 17.05% and 21.38% respectively. And the total error rate is 19.54% relative to full palmprint images.

Human activity recognition and object detection are techniques for detecting a human and objects from an image respectively. Which can be greatly useful for medical, security and industrial fields. This paper presents a new approach by combining both the techniques for extra usability and accurate detection of activities. This paper presents the HOG features for detecting the human in an image and SURF features for extraction of matched points from the image. The accuracy of results are higher due to combination of both the techniques by merging the results of features obtained from an image. The algorithm helps to perform better in low-light conditions which helps to perform better feature extraction. This technique determines the distance of the object from a human and provides the results of activities based on the distance.

Because of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint recognition methods. This paper describes a new multispectral palmprint recognition method that aims to further improve the performance of coding-based approaches by focusing on the local binary pattern (LBP) filters and spiral moments features. The final feature map is derived through a staged process of creating a composite of spiral and LBP features by fusing them together and passing the features through the minimum redundancy maximum relevance transformers. Using Hamming distances, the inter-and intra-similarities of the palmprint feature maps are determined. The experimental technique was evaluated using the available data on the IITD, MSPolyU and PolyU PPDB databases. The results indicate that the method achieved high levels of accuracy in the identification and verification modes. Furthermore, this method outperforms the existing advanced techniques.

Reliable identification notification is still a problem that needs solution in today's world. This thesis study was developed to contribute solutions of that problem in the light of algorithm and biometric identification notification and verification systems. Palm print traces an important role in biometric identification notification. Visions that were obtained from palm print scanner, translated numerical status with using image processing technics. To determine palm print trace attribute vectors, palm trace's referenced working region of interest omitted and viii ŞEKİLLER SAYFA NO

The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an emerging technology that has drawn considerable attention from researchers and scientists over the last decade. In the present work, a novel feature extraction methodology named GPWLD combing the Gabor features with positional Weber's local descriptor (PWLD) features is proposed. WLD is a highly representative micro-pattern descriptor that performs well against noise and illumination changes in images. However, it lacks the ability to capture the vein pattern features at different orientations. Moreover, its descriptor packs the local information content into a single histogram that does not take the spatial locality of micro-patterns into consideration. To solve these two issues, firstly, the palm vein image is passed through Gabor filter with different orientations to capture the salient rotational features found in the output feature maps. Secondly, the spatiality is achieved by uniformly dividing each feature map into several blocks. Next, Weber's law feature descriptor (WLD) histogram is computed for each block in every feature map. Finally, these histograms are concatenated to compose the final feature vector. Due to the high dimensionality of the final feature vector, Principal component analysis (PCA) algorithm is utilized for feature size reduction. In the classification stage, a deep neural network (DNN) comprising an optimized stacked autoencoder (SAE) with Bayesian optimization and a softmax layer is used. Optimization of the SAE is carried out by using Bayesian optimization to find the optimal SAE structure and the options of the training algorithm. The Experimental results verify the discriminative power of the extracted features and the accuracy of the proposed DNN. For both Identification and verification experiments, the proposed method attains higher identification rate and lower EER than state-of-the-art methods. INDEX TERMS WLD descriptor, Gabor filter, deep neural network, stacked autoencoder, Bayesian optimization.

Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between pr...

In this research, images of Kızıltepe (Mardin) agricultural land are classified based on development in different period of crops (1-sowing term, 2-little ripe term, 3-ripe term and 4-harvest term). The digital crop images are derived from TARIT (Agricultural Crop Forecast and Following Project) stations. The texture feature vectors are obtained from images of different crop periods with using Gray Level Co-Occurrence Matrix (GLCM) and Law's Texture Energy Measure (TEM) which are very popular methods for image texture extraction. GLCM and TEM based feature vectors are applied to different type of classifiers as inputs and the performance results of each system are compared. For classification the crop images, Multilayer Perceptron (MLP) neural network, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) methods are used. While the best performance is observed as 100% in GLCM based SVM classifier only, the same performance is observed in TEM based all classifier types. Texture features of crop images based on TEM method allow us to exhibit high performance in all classifiers.

Bu çalışmada, histopatolojik imgelerden çeşitli hastalıkların tanınması işleminde, hekime yardımcı olacak ve kolaylık sağlayacak bir karar destek sistemi tasarlanmıştır. Otomatik hücre sayma sisteminde, hekimler için uzun zaman ve dikkat gerektiren uygulamaların daha kısa sürede ve istenen başarım ölçütleri içinde yapılması sağlanmıştır. Geliştirilen sistem, histopatolojik imgeden istenen hücrelerin ayrıştırılmasını sağlayan bölütleme, söz konusu hücrelerin daha görünür hale getirilmesinde iyileştirme ve istenen hücrelerin sayılması aşamalarını içermektedir. Otomatik hücre sayma sisteminin hepatitli ve kanserli hastalardan alınan histopatolojik imgeler üzerinde değerlendirmesi yapılmıştır. Elde edilen sonuçlar, hekim bulguları ile karşılaştırılarak, %90 oranında doğruluk örtüşmesi sağlanmıştır. In this study, a decision support system is designed which makes things easier for physicians and helps them for recognizing various diseases from histopathologic images. In automatic enumera...

Biometric recognition by hand geometry has a large number of measurements that may be used for authentication. The higher number of attributes, the harder is to define the importance of each one. In this paper, we analyze the use of a Genetic Algorithm-based approach in improving Equal Error Rate (EER) performance for biometric authentication by hand geometry. We used an own data set of dorsal and palm images of hand in a controlled environment to validate our approach. As the best results, the genetic algorithm decreased the equal error rate up to 0% in the training set and 0.01% for the test set. Additionally, a relative improvement of 90.91% was achieved by GA in the best case for the test set.

This paper proposes an automated system for recognizing palmprints for biometric identification of individuals. Complex Zernike moments are constructed using a set of complex polynomials which form a complete orthogonal basis set defined on the unit disc. Palmprint images are projected onto the basis set resulting in a set of complex signals. The magnitude of the complex value is computed and a scalar value is derived from it by computing the mean of the vector elements. Classification is done by subtracting the test samples from the mean of the training set. The data set consists of 80 images divided into 4 classes. Accuracy obtained is comparable to the best results reported in literature

Penelitian ini bertujuan untuk mengetahui: (1) Mengetahui penilaian menurut ahli materi terhadap keanekaragaman odonata (capung) di sawah dan sungai tanjung rahu untuk pengembangan lembar kerja peserta didik (LKPD) berbasis pendekatan ilmiah pada materi insekta; (2) Mengetahui penilaian menurut ahli pembelajaran terhadap keanekaragaman odonata (capung) di sawah dan sungai tanjung rahu untuk pengembangan lembar kerja peserta didik (LKPD) berbasis pendekatan ilmiah pada materi insekta; (3) Mengetahui penilaian menurut ahli desain terhadap keanekaragaman odonata (capung) di sawah dan sungai tanjung rahu untuk pengembangan lembar kerja peserta didik (LKPD) berbasis pendekatan ilmiah pada materi insekta; (4) Mengetahui tanggapan menurut guru biologi terhadap keanekaragaman odonata (capung) di sawah dan sungai tanjung rahu untuk pengembangan lembar kerja peserta didik (LKPD) berbasis pendekatan ilmiah pada materi insekta; (5) Mengetahui tanggapan siswa terhadap keanekaragaman odonata (cap...

In this paper, the author propose palm print recognition algorithm of human being based on radon transform. We propose a general framework for Directional and Texture Features. Firstly we use canny edge detection on taken image. Normally palm prints are recognized by changes in ridges, singular points, minutiae and bifurcation etc. For palm print recognition we use radon transform which is little bit different than other techniques. The proposed algorithm gives us good feature vector of palm print images.

Over the past two decades, several studies have paid great attention to biometric palmprint recognition. Recently, most methods in literature adopted deep learning due to their high recognition accuracy and the capability to adapt with different acquisition palmprint images. However, high-dimensional data with a large number of uncorrelated and redundant features remain a challenge due to computational complexity issues. Feature selection is a process of selecting a subset of relevant features, which aims to decrease the dimensionality, reduce the running time, and improve the accuracy. In this paper, we propose efficient unimodal and multimodal biometric systems based on deep learning and feature selection. Our approach called simplified PalmNet–Gabor concentrates on the improvement of the PalmNet for fast recognition of multispectral and contactless palmprint images. Therefore, we used Log-Gabor filters in the preprocessing to increase the contrast of palmprint features. Then, we ...

Literatürde, parmak izinin belirli bir bölgesi analiz edilerek cinsiyet sınıflandırma üzerine istatistiksel analiz çalışmaları mevcuttur. Bu çalışmalarda parmak izi tepe çizgisi sayılarının kullanıldığı belirlenmiştir. Çalışmalar kısıtlı veriyle yapılmıştır. Kullanılan veri ırka veya ülkeye bağımlıdır. İşlemler de el ile yapılmıştır. Bu çalışmada, literatürde ilk kez parmak izi ve cinsiyet arasındaki ilişkiyi belirlemek için yapay sinir ağları modelleri ile parmak izinin tüm öznitelik vektörlerini kullanarak zeki bir model tanımlanmıştır. Daha sonra elde edilen sonuçlar sunulmuştur. Ön analiz çalışmalarımızda, parmak izlerinin değişken miktarda kullanılabilir veri içerdiği gözlenmiştir. Bu çalışmada kullanılan veri miktarı bu değerler göz önünde bulundurularak belirlenmiştir. Yaptığımız testlerde geliştirilen zeki sistemin başarı oranı %72 olarak elde edilmiştir. Sonuçlar parmak izi ve cinsiyet arasındaki ilişkinin yüksek olduğunu göstermektedir.

A biometric system is a pattern recognition system that recognizes a person on the basis of the physiological or behavioral characteristics that the person possesses. There is increasing interest of researchers in the development of fast and accurate personal recognition systems. In this paper, Sliding window method is used to make the system fast by reducing the matching time. The reduction in computation time indirectly reduces the overall comparison time that makes the system fast. Here, 2-D Complex Gabor Wavelet method is used to extract features from palmprint. The extracted features are stored in a feature vector and matched by hamming distance similarity measurement using sliding window approach. Reduction of 74.12% and 90.32% in comparison time is achieved using Sliding window methods. The improvement in time is indicated by experimental results that makes a system rapid.

The palmprint recognition has become a focus in biological recognition and image processing fields. In this process, the features extraction (with particular attention to palmprint principal line extraction) is especially important. Although a lot of work has been reported, the representation of palmprint is still an open issue. In this paper we propose a simple, efficient, and accurate palmprint principal lines extraction method. Our approach consists of six simple steps: normalization, median filtering, average filters along four prefixed directions, grayscale bottom-hat filtering, combination of bottom-hat filtering, binarization and post processing. The contribution of our work is a new method for palmprint principal lines detection and a new dataset of hand labeled principal lines images (that we use as ground truth in the experiments). Preliminary experimental results showed good performance in terms of accuracy with respect to three methods of the state of the art.

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.

Computer vision approaches like shape based descriptors use silhouettes of objects in images. In this paper, a method to extract silhouettes of objects by segmenting images is proposed in order to describe them, especially characters that are obtained from natural images by using shape based descriptors. This method is binary segmentation approach that has a graph-based representation. Dominant intensity values of segments of an image and cut off intensity value to separate those segments are computed dynamically. Thus, characters that have similar dominant intensity value to the background can be segmented as well. Moreover, the performance of the proposed graph based method is compared with the performance of the global thresholding and it is observed that the success of the proposed method is better than the global thresholding.

Recently, researchers have been paying an excessive amount of attention to biometric palmprint which has gained popularity and received significant prominence due to its high stability and uniqueness. In this study, two certain filters have been taken into consideration, namely Gabor filter and Circular Gabor filter which are used to obtain feature information from two distinguishing regions of interest, square and inscribed circle areas as the central part sub-images and the two palmprint images are compared with one another in terms of their hamming distance. The outcomes of the experiment gave an indication to the fact that circular Gabor Filter had a comparatively better performance than traditional one in extracting distinct feature information.

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.

Biometrics authentication has been used in many applications such as e-commerce, access control etc. In this research, multimodal biometric authentication in light of score level combination of fingerprint and face is proposed. Face and fingerprint is two of the most popular biometric traits and can complement each other for more reliable user authentication. To verify the effectiveness of the proposed method, FERET, SDUMLA-HMT and FVC2002 databases are used in this research. Furthermore, experimental results showed that the proposed fusion based method for fingerprint and face recognition can improve the performance in terms of Equal Error Rate (EER). Eventually, the experimental result shows that our proposed fusion based technique appears to be promising with 7.01~9.13% of EER based on the different datasets.

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.

Barrier synchronization is widely used in shared-memory parallel programs to synchronize between phases of data-parallel algorithms. With proliferation of many-core processors, barrier synchronization has been adapted for higher level language abstractions in new languages such as X10 wherein the processes participating in barrier synchronization are not known a priori, and the processes in distinct “places” don’t share memory. Thus, the challenge here is to not only achieve barrier synchronization in a distributed setting without any centralized controller, but also to deal with dynamic nature of such a synchronization as processes are free to join and drop out at any synchronization phase. In this paper, we describe a solution for the generalized distributed barrier synchronization wherein processes can dynamically join or drop out of barrier synchronization; that is, participating processes are not known a priori. Using the policy of permitting a process to join only in the begin...

Carotid artery stenosis is usually the bottleneck caused by atherosclerosis or carotid artery luminal narrowing. Carotid arteries are in close proximity to bone and bony structures as spongy. Contrast-enhanced computerized tomography angiography (CTA) is used to monitor and measure carotid arteries under the control of an expert. Recently, there is a strong and growing demand for improving the computer aided carotid segmentation process. Recently, there has been a strong and growing demand for computer aided carotid segmentation. In this study, segmentation of the vessels in the CTA images is performed by using region-based active contours method and classification of the segmented regions. The boundaries of the vessel-bone regions are found by region-based active contour segmentation. After segmentation of regions with high gray-scale level values, such as veins and bones, these regions should be separated from each other. In order to perform only carotid artery segmentation in CTA images, it is necessary to eliminate bone fragments and noisy vessel-like structures. For this purpose, in this study, a decision-making mechanism at the point of vein-bone separation is established to classify the segmented regions with a supervised learning system. The method is applied on different patients' CTA images and the performance evaluation is done with statistical and area-based metrics. In these experimental results; average of over 89% Dice similarity 99% accuracy are obtained.

Biometrics which use of human physiological characteristics for identifying an individual is now a widespread method of identification and authentication. Biometric identification is a technology which uses several image processing techniques and describes the general procedure for identification and verification using feature extraction, storage and matching from the digitized image of biometric characters such as Finger Print, Face, Iris or Palm Print. The current paper uses palm print biometrics. Here we have presented an identification approach using textural properties of palm print images. The elegance of the method is that the conventional edge detection technique is extended to suitably describe the texture features. In this technique all the characteristics of the palm such as principal lines, edges and wrinkles are considered with equal importance.