Intrinsic biometric, nowadays, has become a trend
in research on human identification due to some disadvantages of
the extrinsic biometric features. Extrinsic biometric features are
easily imitated and lost as they are located outside the human
body and are easy to change due to accidents. Therefore, in this
paper we focus on a method which can extract a feature from an
image of intrinsic biometric. Moreover, we use palm skin vein as
the intrinsic biometric feature for human recognition application.
The feature of an image can be extracted by using a specific
method, such as Local binary pattern (LBP), which has been
commonly used in many research works. A modified LBP, called
cross-LBP (DVHLBP), has been proposed in our previous paper.
DVHLBP has better performance compared with the
conventional LBP. In this paper, we further optimize the
DVHLBP method. In this paper, DVHLBP is used as the
extraction feature algorithm on palm vein and histogram
intersection is used for the matching process. In the simulation,
the ratio of data model to data testing was 5:5. Testing was done
by applying some scenarios. The optimization is done by
examining the number of regions that yield the optimal threshold
value. The optimal configuration is achieved when we use 8
neighborhood pixels with radius of 12, 16 regions. Simulation
results show that the false accepted rate (FAR) and false rejected
rate (FRR) are 0.01 and 0.01, respectively, with recognition rate
of 99%. In addition, we show that the optimized DVHLBP has
improvement in the accuracy and equal error rate (EER).
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
Iris recognition is known as an inherently reliable biometric technique for human identification. Feature extraction is a crucial step in iris recognition, and the trend nowadays is to reduce the size of the extracted features. Special efforts have been applied in order to obtain low templates size and fast verification algorithms. These efforts are intended to enable a human authentication in small embedded systems, such as an Integrated Circuit smart card. In this paper, an effective eyelids removing method, based on masking the iris, has been applied. Moreover, an efficient iris recognition encoding algorithm has been employed. Different combination of wavelet coefficients which quantized with multiple quantization levels are used and the best wavelet coefficients and quantization levels are determined. The system is based on an empirical analysis of CASIA iris database images. Experimental results show that this algorithm is efficient and gives promising results of False Accept Ratio (FAR) = 0% and False Reject Ratio (FRR) = 1% with a template size of only 364 bits.
A Spectral Domain Dominant Feature Extraction Algorithm for Palm-print Recogn...CSCJournals
In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out using two dimensional Fourier transform within those spatial modules. A dominant spectral feature selection algorithm is proposed, which offers an advantage of very low feature dimension and results in a very high within-class compactness and between-class separability of the extracted features. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
MELANOMA CELL DETECTION IN LYMPH NODES HISTOPATHOLOGICAL IMAGES USING DEEP LE...sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
Iris recognition is known as an inherently reliable biometric technique for human identification. Feature extraction is a crucial step in iris recognition, and the trend nowadays is to reduce the size of the extracted features. Special efforts have been applied in order to obtain low templates size and fast verification algorithms. These efforts are intended to enable a human authentication in small embedded systems, such as an Integrated Circuit smart card. In this paper, an effective eyelids removing method, based on masking the iris, has been applied. Moreover, an efficient iris recognition encoding algorithm has been employed. Different combination of wavelet coefficients which quantized with multiple quantization levels are used and the best wavelet coefficients and quantization levels are determined. The system is based on an empirical analysis of CASIA iris database images. Experimental results show that this algorithm is efficient and gives promising results of False Accept Ratio (FAR) = 0% and False Reject Ratio (FRR) = 1% with a template size of only 364 bits.
A Spectral Domain Dominant Feature Extraction Algorithm for Palm-print Recogn...CSCJournals
In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out using two dimensional Fourier transform within those spatial modules. A dominant spectral feature selection algorithm is proposed, which offers an advantage of very low feature dimension and results in a very high within-class compactness and between-class separability of the extracted features. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
MELANOMA CELL DETECTION IN LYMPH NODES HISTOPATHOLOGICAL IMAGES USING DEEP LE...sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a
controllable field by individuals. Therefore a technology, software products is needed. In this paper we
focused on what we have done about crowd analysis and examination the problem of human detection with
fish-eye lenses cameras. In order to identify human density, one of the machine learning algorithm, which
is Haar Classification algorithm, is used to distinguish human body under different conditions. First,
motion analysis is used to search for meaningful data, and then the desired object is detected by the trained
classifier. Significant data has been sent to the end user via socket programming and human density
analysis is presented.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Non negative matrix factorization ofr tuor classificationSahil Prajapati
The PPT aware about you the concept of Non Negative Matrix Factorization and how theses techniques can be used to treat cancer by the use of the coding such as a MATLAB,LABVIEW software to locate the tumor or the cancer part with the different approaches and tachniques.
Go through the PPT to know and how one can improvise my work for better results??
Please help me if one come up with other techniques.
Symbolic representation and recognition of gait an approach based on lbp of ...sipij
Gait is one of the biometric techniques used to identify an individual from a distance by his/her walking
style. Gait can be recognized by studying the static and dynamic part variations of individual body contour
during walk. In this paper, an interval value based representation and recognition of gait using local
binary pattern (LBP) of split gait energy images is proposed. The gait energy image (GEI) of a subject is
split into four equal regions. LBP technique is applied to each region to extract features and the extracted
features are well organized. The proposed representation technique is capable of capturing variations in
gait due to change in cloth, carrying a bag and different instances of normal walking conditions more
effectively. Experiments are conducted on the standard and considerably large database (CASIA database
B) and newly created University of Mysore (UOM) gait dataset to study the efficacy of the proposed gait
recognition system. The proposed system being robust to handle variations has shown significant
improvement in recognition rate.
Shape and Level Bottles Detection Using Local Standard Deviation and Hough Tr...IJECEIAES
This paper presents shape and level analysis using local standard deviation and Hough transform technique to detect the shape and level of the bottle. A 155 sample images are used as a test product to detect shape and level. Local standard deviation is used contrast gain technique to segment the shape of the bottle by enhancing the contrast of the image. The ratio of the area is calculated from the extent parameter. The maximum and minimum water level is created by using Hough transform technique to identify the level of the water. Decision tree is applied to classify the shape and level of the bottle either good or defect condition. From experimental result, 97% and 93% accuracy of shape and level is achieved which shows that the proposed analysis technique is potential to be applied for beverages product inspection system.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
RANSAC BASED MOTION COMPENSATED RESTORATION FOR COLONOSCOPY IMAGESsipij
Colonoscopy is a procedure that has been used widely to detect the abnormality in a colon. Colonoscopy images suffer from a lot of problems that make it hard for the doctor to investigate/ understand a colon patient. Unfortunately, with the current technology, three is no way for doctors to know if the whole colon surface has been investigated or not. We have developed a method that utilizes RANSAC-based image registration to align sequences of any length in the colonoscopy video and restores each frame of the video using information from these aligned images. We proposed two methods. First method used the deep neural net for the classification of informative and non-informative image. The classification result was used as a preprocessing for alignment method. Also, we proposed a visualization structure for the classification results. The second method used the alignment to decide/classify the bad and good alignment by using two factors. The first factor is the accumulated error and the second factor contain three checking steps that check the pair error alignment beside the geometry transform status. The second method was able to align long sequences.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a
controllable field by individuals. Therefore a technology, software products is needed. In this paper we
focused on what we have done about crowd analysis and examination the problem of human detection with
fish-eye lenses cameras. In order to identify human density, one of the machine learning algorithm, which
is Haar Classification algorithm, is used to distinguish human body under different conditions. First,
motion analysis is used to search for meaningful data, and then the desired object is detected by the trained
classifier. Significant data has been sent to the end user via socket programming and human density
analysis is presented.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Non negative matrix factorization ofr tuor classificationSahil Prajapati
The PPT aware about you the concept of Non Negative Matrix Factorization and how theses techniques can be used to treat cancer by the use of the coding such as a MATLAB,LABVIEW software to locate the tumor or the cancer part with the different approaches and tachniques.
Go through the PPT to know and how one can improvise my work for better results??
Please help me if one come up with other techniques.
Symbolic representation and recognition of gait an approach based on lbp of ...sipij
Gait is one of the biometric techniques used to identify an individual from a distance by his/her walking
style. Gait can be recognized by studying the static and dynamic part variations of individual body contour
during walk. In this paper, an interval value based representation and recognition of gait using local
binary pattern (LBP) of split gait energy images is proposed. The gait energy image (GEI) of a subject is
split into four equal regions. LBP technique is applied to each region to extract features and the extracted
features are well organized. The proposed representation technique is capable of capturing variations in
gait due to change in cloth, carrying a bag and different instances of normal walking conditions more
effectively. Experiments are conducted on the standard and considerably large database (CASIA database
B) and newly created University of Mysore (UOM) gait dataset to study the efficacy of the proposed gait
recognition system. The proposed system being robust to handle variations has shown significant
improvement in recognition rate.
Shape and Level Bottles Detection Using Local Standard Deviation and Hough Tr...IJECEIAES
This paper presents shape and level analysis using local standard deviation and Hough transform technique to detect the shape and level of the bottle. A 155 sample images are used as a test product to detect shape and level. Local standard deviation is used contrast gain technique to segment the shape of the bottle by enhancing the contrast of the image. The ratio of the area is calculated from the extent parameter. The maximum and minimum water level is created by using Hough transform technique to identify the level of the water. Decision tree is applied to classify the shape and level of the bottle either good or defect condition. From experimental result, 97% and 93% accuracy of shape and level is achieved which shows that the proposed analysis technique is potential to be applied for beverages product inspection system.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
RANSAC BASED MOTION COMPENSATED RESTORATION FOR COLONOSCOPY IMAGESsipij
Colonoscopy is a procedure that has been used widely to detect the abnormality in a colon. Colonoscopy images suffer from a lot of problems that make it hard for the doctor to investigate/ understand a colon patient. Unfortunately, with the current technology, three is no way for doctors to know if the whole colon surface has been investigated or not. We have developed a method that utilizes RANSAC-based image registration to align sequences of any length in the colonoscopy video and restores each frame of the video using information from these aligned images. We proposed two methods. First method used the deep neural net for the classification of informative and non-informative image. The classification result was used as a preprocessing for alignment method. Also, we proposed a visualization structure for the classification results. The second method used the alignment to decide/classify the bad and good alignment by using two factors. The first factor is the accumulated error and the second factor contain three checking steps that check the pair error alignment beside the geometry transform status. The second method was able to align long sequences.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
An Enhanced Biometric System for Personal AuthenticationIOSR Journals
Palm vein authentication is a new and latest biometric method utilizing the vein patterns inside one’s
palm for personal identity verification. Palm patterns are different for each person.and as they are hidden
underneath the skin’s surface,forgery is extremely difficult.Infrared light is used to capture an image of a palm
that shows the vein patterns,which have various widths and brightness that change temporally as a result of
fluctuations in the amount of blood in the vein,depending on temperature,physical conditions,etc.To robustly
extract the precise details of the depicted veins, we developed a method of Anisotropic technique in crosssectional
profiles of a vein image.This method can extract the centrelines of the veins consistently without being
affected by the fluctuations in vein width and brightness,so its pattern matching is highly accurate. This paper
discusses the origins, feature extraction, technology,applications of palm vein authentication. The proposed
system include: 1) Infrared palm images capture; 2) Detection of Region of Interest; 3) Palm vein extraction by
Anisotropic filtering; 4) Matching. The experimental results demonstrate that the recognition rate using palm
vein is good.
An Enhanced Biometric System for Personal AuthenticationIOSR Journals
Abstract- Palm vein authentication is a new and latest biometric method utilizing the vein patterns inside one’s palm for personal identity verification. Palm patterns are different for each person.and as they are hidden underneath the skin’s surface,forgery is extremely difficult.Infrared light is used to capture an image of a palm that shows the vein patterns,which have various widths and brightness that change temporally as a result of fluctuations in the amount of blood in the vein,depending on temperature,physical conditions,etc.To robustly extract the precise details of the depicted veins, we developed a method of Anisotropic technique in cross-sectional profiles of a vein image.This method can extract the centrelines of the veins consistently without being affected by the fluctuations in vein width and brightness,so its pattern matching is highly accurate. This paper discusses the origins, feature extraction, technology,applications of palm vein authentication. The proposed system include: 1) Infrared palm images capture; 2) Detection of Region of Interest; 3) Palm vein extraction by Anisotropic filtering; 4) Matching. The experimental results demonstrate that the recognition rate using palm vein is good. Index Terms-Palm vein, Liveness detection, Infrared palm images, Anisotropic filtering.
In the present day automation, the researchers have been using microcomputers and its allies to carryout processing of physical quantities and detection of Cholesterol in blood and bio-medical Images. The latest trend is to use FPGA counter parts, as these devices offer many advantages in comparison with Programmable devices. These devices are very fast and involve hardwired logic. FPGA are dedicated hardware for processing logic and do not have an operating system. That means that speeds can be very fast and multiple control loops can run on a single FPGA device at different rates. In this paper, an attempt is being made to develop a prototype system to sense the Cholesterol portion in MRI image using modified Set Partitioning in Hierarchical Trees (SHIPT) wavelets transformation and Radial Basis Function (RBF). An each stage of Cholesterol detection are displayed on LCD monitor for clear view of improved version of MRI image and to find Cholesterol area. The performance parameters have been measured in terms of Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE).
Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a controllable field by individuals. Therefore a technology, software products is needed. In this paper we focused on what we have done about crowd analysis and examination the problem of human detection with fish-eye lenses cameras. In order to identify human density, one of the machine learning algorithm, which is Haar Classification algorithm, is used to distinguish human body under different conditions. First, motion analysis is used to search for meaningful data, and then the desired object is detected by the trained classifier. Significant data has been sent to the end user via socket programming and human density analysis is presented.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Top Cited Articles in Signal & Image Processing 2021-2022sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
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Palm Vein Feature Extraction Method by Using Optimized DVHLocal Binary Pattern
1. Palm Vein Feature Extraction Method by Using
Optimized DVHLocal Binary Pattern
Dini Fronitasari1
, Basari2
and Dadang Gunawan3
Department of Electrical of Engineering
Universitas Indonesia
Depok, Indonesia
dini.fronitasari61@ui.ac.id
Abstract— Intrinsic biometric, nowadays, has become a trend
in research on human identification due to some disadvantages of
the extrinsic biometric features. Extrinsic biometric features are
easily imitated and lost as they are located outside the human
body and are easy to change due to accidents. Therefore, in this
paper we focus on a method which can extract a feature from an
image of intrinsic biometric. Moreover, we use palm skin vein as
the intrinsic biometric feature for human recognition application.
The feature of an image can be extracted by using a specific
method, such as Local binary pattern (LBP), which has been
commonly used in many research works. A modified LBP, called
cross-LBP (DVHLBP), has been proposed in our previous paper.
DVHLBP has better performance compared with the
conventional LBP. In this paper, we further optimize the
DVHLBP method. In this paper, DVHLBP is used as the
extraction feature algorithm on palm vein and histogram
intersection is used for the matching process. In the simulation,
the ratio of data model to data testing was 5:5. Testing was done
by applying some scenarios. The optimization is done by
examining the number of regions that yield the optimal threshold
value. The optimal configuration is achieved when we use 8
neighborhood pixels with radius of 12, 16 regions. Simulation
results show that the false accepted rate (FAR) and false rejected
rate (FRR) are 0.01 and 0.01, respectively, with recognition rate
of 99%. In addition, we show that the optimized DVHLBP has
improvement in the accuracy and equal error rate (EER).
Keywords— Biometric, palm vein recognition, pattern
recognition, local binary pattern (LBP), diagonal vertical
horizontal local binary pattern (DVHLBP), KNN.
I. INTRODUCTION
A biometric systems, recently, has become more
reliable in as recognition system, in which the physical and
behavioral characteristics of an individual are made to be
the unique features. One of the unique features commonly
used as the biometric feature is the palm vein.
A palm vein is popular for its special structure pattern
as the representation of an individual. A palm vein is the
intrinsic feature of an individual which has more benefits
than other features, e.g. it is difficult to be forged because it
lies under the skin, it is uncorrupted, and it cannot be used
for a dead individual. We cannot use an ordinary camera or
bare eyes to capture a palm vein image. Instead, a special
near infrared (NIR) camera is required [1].
Biologically, a vein is a part of body circulation that
delivers blood from heart to other organs in the whole body
and vice versa. As mentioned before, a vein network at
human’s hand has its unique feature. Each individual has
his/her own unique vein network pattern. This encourages
many researchers to work on palm vein pattern, where the
pattern is used as the input for a biometric system [3].
Research works on biometric system, especially palm
vein, have been intensively explored by many researchers
until today [2][3][4][5]. Hence, in this paper, we improve
the performance of palm vein identification system by
optimizing the cross local binary pattern (DVHLBP)
algorithm. DVHLBP algorithm has been used as the feature
extractor to improve the system performance. It has been
shown in our previous work [10] that the DVHLBP method
could achieve better performance than the method
presented in [11]. The aim of this paper is to optimize the
parameters of the DVHLBP.
Figure 1. Palm Vein Recognition
The rest of the paper is organized as follows. Section II
discusses the palm vein image preprocessing technique.
Section III presents the feature extraction by using
DVHLBP algorithm. Section IV discusses the matching
process by using the histogram intersection while the
testing scenarios appear in Section V. Finally, Section VI
provides the conclusion of this paper.
II. PREPROCESSING
In a biometric system, acquisition of an image with
unique feature is essential. We use a specific part of a palm
vein image, i.e. the middle part, to get an optimal feature
extraction. Region of interest (ROI) of the palm image
extracts the texture of the vein pattern and must be
determined beforehand. Boundary line of the hand is selected
before detecting the valley points. Valley points are used to
construct the rectangular ROI containing the vein texture
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 17, No. 5, May 2019
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ISSN 1947-5500
2. pattern. The rectangular ROI is constructed by connecting the
valley points. Figure 1 shows the image preprocessing
diagram.
Binary
Process
Boundary
Line
Detection of
Valley Point
Forming a rectangular
area by make a straight
line v2 and v4
ROI
Selection
V1
V3V2
V4
Figure 2. Preprocessing module.
After ROI selection, the next process is to equalize the
palm vein image size. The equalization is necessary to avoid
the varying size of the palm vein image due to the extraction
feature process. We use 256 x 256 image size for simulations.
The whole procedure for palm vein recognition process is
depicted in Figure 2.
DATA SET
FEATURE
EXTRACTION
PREPROCESSING
MATCHING
FEATURE
EXTRACTION
PREPROCESSINGDATA SET
PALM VEIN
RECOGNITION
Figure 3. Recognition system.
III. FEATURE EXTRACTION USING DVHLPB
As previusly mentioned, we focus on the feature
extraction using DVHLBP method. This method is developed
from the conventional LBP method. The DVHLBP is
employed in a matrix of 3x3 accompanied by 8 neighborhood
pixels and 1 center pixel which is placed in the center of the
matrix. Figure 3 shows the description of the DVHLBP
flowchart.
A. Build Index Tabel
The index table is the main domain to save the selected
pixel value after DVHLBP process. DVHLBP provides a
certain value after comparing the neighbors of a pixel. Then,
the value is stored in the index table.
B. DVHLBP Method
DVHLBP is a method developed form LBP. The
process is similar to LBP. We compare the value of a pixel
with its neighborhood pixels resulting in a group of bits
which is converted to decimal numbers. Figure 3 shows the
process of DVHLBP.
Figure 4. DVHLBP Method
(1)
The binary threshold function is given as
DVHLBP using linear interpolation of the pixel values
allows the choice of any radius (R) and a number of pixels in
the neighborhood (P).
C. Region Cutting
Palm vein image is split into smaller parts as local features.
Each part is extracted by LBP extraction and it takes
histogram feature (as global feature) of the palm vein image.
By splitting the image into smaller parts, the accuracy of the
divided portion of the data model and data testing increases.
The increase is caused by the matching process done in each
region of the data model to each region of data testing. This
concept has been aplied in [3] where LBP Multi block concept
was used for face recognition system yielding recognition rate
up to 97%. The comparison of the similarity was better than 2
images testing through seperated regions. In this research, we
split 16 regions of a palm vein image before the feature
extraction process.
D. Histogram Extraction
The results of DVHLBP process has been obtained at
each region of palm vein image. Then, the feature of
histogram is extracted. The histogram values are concatenated
to become one feature. In this research, histogram value of 16
regions is extracted and collected to be one feature. The 16
regions of histogram are merged into one feature.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 17, No. 5, May 2019
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ISSN 1947-5500
3. …….
Figure 5. Histogram Extraction and Merging Histogram Feature.
IV. MATCHING
A. Similarity Measurement
Histogram intersection method is chosen to measure the
similarity between two images for identification. The
intersection of histogram provides higher value for identifying
the performance, since no background and foreground are
necessarily divided [7] [8].
( )
q
qp
i
i
ii
qpH
∑
=
,min
),(
(2)
The compared histograms are respectively represented by p
and q, and i which is the binary of the histogram [6]. The
result of histogram intersection is between 0 and 1. The higher
the value, the closer it is to 1 and the higher the similarity of
the two histograms.
B. Decision
The main problem of the research of palm vein
recognition is whether the data testing is registered/accepted
by the system or unregistered/rejected by the system.
Acceptation/rejection is done by a threshold value which is
determined before. If the result of matching is more than or
equal to the threshold value, the data testing is accepted. If the
result of matching is less than or equal to the threshold value,
the data testing is rejected. The threshold value is determined
by some calculations. Firstly, threshold array (TA) is obtained
by calculating the histogram intersection of data model for
each individual. Secondly, minimum and maximum values of
TA are found out. Delta value can be obtained by this formula.
When the threshold is optimal, the system threshold is set
by comparing the difference value of the false rejected rate
(FRR) and false accepted rate (FAR) or the same as the rate of
error on the intersection of FAR and FRR on the region of
convergence (ROC) curve.
V. TESTING SCENARIO
In this simulations, some test scenarios are conducted to
obtain optimal parameters and to examine the system
performance in development. The system of biometric has two
rudimentary forms of faults: FAR and FRR [5]. System
performance analysis is performed with a measurement metric
shown by the ROC curve. The performance is obtained from
the intersection between The FRR and FAR curves denoted as
(ERR) that stands for Equal Error Rate. Smaller ERR value
results in higher performance.
A. Hardware and software specification.
The test run on a computer with an Intel (R) Core (TM) i5
3.1 GHz processor and 4 GB RAM using the Windows 7 64-
bit operating system and MATLAB R2019a simulation tools.
B. Dataset
This study utilizes dataset of CASIA with multi-spectral
palm print that consists of palm vein image of 850 nm (with
850 nm illuminator). The image contains 768 x 576 pixels that
belong to individuals having ID 001 – 050 and 3 samples for
each individual with label 01 – 03.
B. Performence Measurement
The performance measurement was using accuracy, and
FAR and FRR. The accuracy is calculated by using:
Accuracy = x 100% (3)
While FAR and FRR were calculated using the formulae:
(4)
(5)
We use four scenarios to test the palm vein recognition system
as described in Table 1.
Table 1.Examining the scenario
Scenario Description Purpose
Scenario 1 Testing parameters P
(neighboring) and R
(radius) on DVHLBP
method with ratio of model
data and test data of 5:5,
the palm vein image is
divided into 16 regions.
This test is done to
find the optimal P and
R in the DVHLBP
method by viewing the
best accuracy
generated.
Scenario 2 Testing the number of
regions of a palm vein
image, with the ratio of
model data to test data of
5: 5, and the configuration
in scenario 1. In this
scenario, we test the
accuracy of the result
obtained with a number of
varied regions.
The purpose of this test
is to obtain the optimal
number of regions by
looking at the best
accuracy obtained.
Scenario 3 Performing threshold value
search to calculate the
performance of FAR and
FRR values from this
biometrics system, with the
ratio of model data to test
data of 5:5, and the
configuration in scenarios
1 and 2.
This test is aimed to
obtain the optimal
threshold value by
finding the smallest
difference between
FAR and FRR, or FAR
and FRR intersections
on the ROC curve
(equal error rate).
Scenario 4 Examining the variation of
the palm vein image size
adjustment, with the ratio
of model data to test data
of 5:5, and the
configuration in scenarios
1, 2, and 5.
The purpose of this test
is to examine the
performance of the
recognition rate on the
shape of the change of
image size of the
processed palm vein.
International Journal of Computer Science and Information Security (IJCSIS),
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4. VI. TESTING RESULT
A. Scenario Analysis 1
Table 2 shows the results of system accuracy with the
changes in the parameter value tested. It can be seen that the
optimum accuracy is obtained when P = 8 and R = 12.
Table 2. Scenario 1 Test Result
Parameter
Training
Data
Testing
Data
Accuracy
(%)
P=8; R=1 150 150 92%
P=8; R=4 150 150 90%
P=8; R=6 150 150 94%
P=8; R=12 150 150 99%
Table 3. Scenario 1 Test Result
Parameter
Training
Data
Testing
Data
Accuracy
(%)
P=16; R=1 150 150 85%
P=16; R=4 150 150 80%
P=16; R=6 150 150 88%
P=16; R=12 150 150 85%
Table 3 shows the results of accuracy measurements of the
two tested parameters. It can be seen that at the number of
neighbors of 16, we start the test at the number of radius of 4.
This is because the number of radius smaller than 4 produces a
poor performance due to the buildup point neighbors in the
image pixel blocks and the unavailability of the pixel block
area that holds 16 numbers of neighbors in the radius.
It is known that at the number of neighbors of 8, it produces
the best accuracy of 99%. It has increased from our previous
paper [10] by using the same extraction feature with the same
parameter but different in the classification process. When the
number of neighbors is 16, the system yields the best
accuracy, which is 88.00%. Therefore, it can be seen that 16 is
the optimum number of neighbors and 6 is the optimum
radius. By examining that the best accuracy is obtained by the
number of radius of 12, it can be concluded that 8 neighbors
(P = 8) and radius of 12 (R = 12) are the optimal parameter for
DVHLBP configuration.
B. Scenario Analysis 2
Table 4. Scenario 2 Test Result
No.
Number of
Region
Training
Data
Testing
Data
Accuracy
(%)
1 4 150 150 92%
2 16 150 150 99%
3 25 150 150 97%
4 64 100 150 96%
5 128 150 150 88%
Table 4 presents the results of the accuracy measurements
on the number of regions tested. It can be seen that the best
accuracy is 99 with 16 regions, while for the smaller number
of regions, e.g. 4, it results in an accuracy of 92%, and for
larger regions, e.g. 25, 64, and 128, it results in 97%, 96.00%,
and 88% accuracy, respectively. It can be seen that the number
of regions with the addition of the number of regions from 4
regions to 16 regions increases the accuracy but when we
increase the number of regions from 16 regions to 25 and 64
regions, we have a decreasing accuracy. This is due to the
increasing number of regions so that smaller image size will
be processed which effect the complexity of the process of
image matching. Therefore, 16 is the optimal number of
regions which results in accuracy of 99%.
C. Scenario Analysis 3
Scenario 3 is done to examine the performance of the ERR
by showing the point of intersection of FRR and FAR on ROC
curve with the ratio of model data to test data of 3: 3, and
configuration of best parameter in scenarios 1 and 2. FAR
shows the acceptance error on the system where the test data
not listed in the system are considered to be registered in the
system. On the other hand, FRR is a refusal error on the
system where the test data listed are considered not listed in
the system. FAR and FRR are obtained by using a threshold
value that determines the system acceptance and rejection
decision. There are variables that show the number of
thresholds tested (N: 1,2,3, ...,).The table below shows the
optimal threshold value of each value of β that is used.
Table 5. Scenario 3 Test Result
One threshold value selected is 38.2680 because it produces
the lowest FRR and FAR. The bottom most FAR value
specifies the lowest error rate to accept while the lowest value
of FRR denotes the lowest refutation error. The optimal
threshold value is determined by finding the intersection point
between FAR and FRR. Scenario 4 testing result shows
system performance using threshold value 38.2680 from the
total of 150 test data belonging to 50 individuals with each
amounted to 3 samples in the case of identification shown
error rate of 1% and recognition rate of 99%.
D. Scenario 4
Based on the scenario 4, we conduct a test by observing
the size variation of the palm vein image data. The initial size
of the palm vein image data on the system was 256 x 256.
This test is done by changing the data size of the palm vein
image in order to observe the effect to the system accuracy. It
is expected that better performance in terms of accuracy level
can be achieved by adjusting the size of the image. The test
was done using data model ratio and the ratio of 3:3 test data,
threshold value of 0.53530564, and configuration of optimal
parameters in scenario 1 and 2. Table 5 shows the test results
for image data size variations.
β FAR FRR Threshold
25 0.01 0.01 38.2680
50 0.01 0.01 38.2680
75 0.01 0.01 38.2680
100 0.01 0.01 38.2680
International Journal of Computer Science and Information Security (IJCSIS),
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5. Table 5. Scenario 4 Test Result
No Image Size Rate
1 152x152 84%
2 200x200 97%
3 256x256 99%
4 300x300 99%
5 320x320 99%
It can be seen that when we decrease the size of the palm vein
image, the accuracy also decreases by around 13% and 2% for
image sizes of 200 x 200 and 152 x 152, respectively. On the
other hand, when we increase the size of the image, no
changes in the accuracy is observed. In particular, the system
has the same accuracy rate of 99 for image sizes of 256 x 256,
300 x 300, and 320 x 320.
VII. CONCLUSION
In this paper, we have optimized the DVHLBP method used
for palm vein feature extraction [10], which can be more
reliable and achieve higher accuracy as compared to
previously proposed palm vein authentication systems [11].
Our proposed system has worked more leads to more accurate
performance. We have also evaluated the performance of the
optimized DVHLBP method in terms of the accuracy, FAR,
and FRR. The accuracy has improved significantly compared
with our previous method.
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Vol. 17, No. 5, May 2019
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