This document presents a study on using fingerprints to identify the gender of individuals. The study uses a database of 400 fingerprint images (200 male and 200 female). Fingerprints are preprocessed using discrete wavelet transform and converted to binary images. The number of white pixels and total block pixels are counted and converted to binary numbers. The length of these binary numbers is then used to classify gender, with lengths of 15 digits or more indicating male and less than 15 indicating female. Experimental results show an accuracy of 88.75% using this single feature of binary length. The study concludes that fingerprints can effectively be used to determine gender with applications in security and forensic analysis. Future work aims to improve accuracy by collecting more data and extracting additional fingerprint features.
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
This document proposes using a cuckoo search algorithm to optimize the process of fingerprint matching for biometric identification. It begins by introducing biometric recognition and some of its challenges with large and complex datasets. It then provides background on cuckoo search optimization and describes how it can be applied to optimize fingerprint matching. Specifically, it presents an algorithm that extracts sub-matrices of increasing dimension from a fingerprint image matrix and uses cuckoo search to match fingerprints by comparing the sub-matrices until an accurate match is found. The document simulates this algorithm and outlines the results, demonstrating how cuckoo search optimization may help address limitations of traditional techniques for complex biometric analysis.
Extraction of spots in dna microarrays using genetic algorithmsipij
DNA microarray technology is an eminent tool for genomic studies. Accurate extraction of spots is a
crucial issue since biological interpretations depend on it. The image analysis starts with the formation of
grid, which is a laborious process requiring human intervention. This paper presents a method for optimal
search of the spots using genetic algorithm without formation of grid. The information of every spot is
extracted by obtaining a pixel belonging to that spot. The method developed selects pixels of high intensity
in the image, thereby spot is recognized. The objective function, which is implemented, helps in identifying
the exact pixel. The algorithm is applied to different sizes of sub images and features of the spots are
obtained. It is found that there is a tradeoff between accuracy in the number of spots identified and time
required for processing the image. Segmentation process is independent of shape, size and location of the
spots. Background estimation is one step process as both foreground and complete spot are realized.
Coding of the proposed algorithm is developed in MATLAB-7 and applied to cDNA microarray images.
This approach provides reliable results for identification of even low intensity spots and elimination of
spurious spots.
11.0002www.iiste.org call for paper.human verification using multiple fingerp...Alexander Decker
This document presents a multimodal biometric verification system that uses multiple fingerprint matchers for human verification. It proposes combining two fingerprint matching techniques, the Spatial Grey Level Dependence Method (SGLDM) and a Filterbank-based technique, at the score level to generate a final matching score. The SGLDM extracts statistical texture features from fingerprints, while the Filterbank-based technique utilizes both global and local fingerprint features. The individual matching scores from each technique are normalized and combined using the sum rule. Experimental results on a fingerprint database demonstrate that the proposed fusion strategy improves overall accuracy by reducing total error rates compared to the individual matchers.
2.human verification using multiple fingerprint texture 7 20Alexander Decker
This document presents a multimodal biometric verification system that uses multiple fingerprint matchers to improve accuracy. It combines two fingerprint matching techniques - Spatial Grey Level Dependence Method (SGLDM) and Filterbank-based matching. Matching scores from the two techniques are normalized and combined using sum rule fusion. The system was tested on a fingerprint database and experimental results showed the proposed fusion strategy reduces total error rate, improving overall system accuracy compared to single matcher systems.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
The document presents a highly secured palm print authentication system using undecimated bi-orthogonal wavelet (UDBW) transform. The proposed system has three main modules: registration, testing, and palm matching. In the registration module, morphological operations and region of interest extraction are used to preprocess palm images. Distance transform and 3-level UDBW transform are then used to extract low-level features and create feature vectors for registered palm prints. In testing, low-level features are extracted from input palm prints using the same approach. Palm matching involves comparing feature vectors of registered and input palm prints to identify matches. Simulation results show the system provides accurate recognition rates for palm print authentication.
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
This document proposes using a cuckoo search algorithm to optimize the process of fingerprint matching for biometric identification. It begins by introducing biometric recognition and some of its challenges with large and complex datasets. It then provides background on cuckoo search optimization and describes how it can be applied to optimize fingerprint matching. Specifically, it presents an algorithm that extracts sub-matrices of increasing dimension from a fingerprint image matrix and uses cuckoo search to match fingerprints by comparing the sub-matrices until an accurate match is found. The document simulates this algorithm and outlines the results, demonstrating how cuckoo search optimization may help address limitations of traditional techniques for complex biometric analysis.
Extraction of spots in dna microarrays using genetic algorithmsipij
DNA microarray technology is an eminent tool for genomic studies. Accurate extraction of spots is a
crucial issue since biological interpretations depend on it. The image analysis starts with the formation of
grid, which is a laborious process requiring human intervention. This paper presents a method for optimal
search of the spots using genetic algorithm without formation of grid. The information of every spot is
extracted by obtaining a pixel belonging to that spot. The method developed selects pixels of high intensity
in the image, thereby spot is recognized. The objective function, which is implemented, helps in identifying
the exact pixel. The algorithm is applied to different sizes of sub images and features of the spots are
obtained. It is found that there is a tradeoff between accuracy in the number of spots identified and time
required for processing the image. Segmentation process is independent of shape, size and location of the
spots. Background estimation is one step process as both foreground and complete spot are realized.
Coding of the proposed algorithm is developed in MATLAB-7 and applied to cDNA microarray images.
This approach provides reliable results for identification of even low intensity spots and elimination of
spurious spots.
11.0002www.iiste.org call for paper.human verification using multiple fingerp...Alexander Decker
This document presents a multimodal biometric verification system that uses multiple fingerprint matchers for human verification. It proposes combining two fingerprint matching techniques, the Spatial Grey Level Dependence Method (SGLDM) and a Filterbank-based technique, at the score level to generate a final matching score. The SGLDM extracts statistical texture features from fingerprints, while the Filterbank-based technique utilizes both global and local fingerprint features. The individual matching scores from each technique are normalized and combined using the sum rule. Experimental results on a fingerprint database demonstrate that the proposed fusion strategy improves overall accuracy by reducing total error rates compared to the individual matchers.
2.human verification using multiple fingerprint texture 7 20Alexander Decker
This document presents a multimodal biometric verification system that uses multiple fingerprint matchers to improve accuracy. It combines two fingerprint matching techniques - Spatial Grey Level Dependence Method (SGLDM) and Filterbank-based matching. Matching scores from the two techniques are normalized and combined using sum rule fusion. The system was tested on a fingerprint database and experimental results showed the proposed fusion strategy reduces total error rate, improving overall system accuracy compared to single matcher systems.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
The document presents a highly secured palm print authentication system using undecimated bi-orthogonal wavelet (UDBW) transform. The proposed system has three main modules: registration, testing, and palm matching. In the registration module, morphological operations and region of interest extraction are used to preprocess palm images. Distance transform and 3-level UDBW transform are then used to extract low-level features and create feature vectors for registered palm prints. In testing, low-level features are extracted from input palm prints using the same approach. Palm matching involves comparing feature vectors of registered and input palm prints to identify matches. Simulation results show the system provides accurate recognition rates for palm print authentication.
Cursive Handwriting Recognition System using Feature Extraction and Artif...IRJET Journal
The document describes a system for recognizing cursive handwriting using feature extraction and an artificial neural network. It involves preprocessing scanned images, segmenting them into individual characters, extracting features from the characters using a diagonal scanning method, and classifying the characters using a neural network. This approach provides higher recognition accuracy compared to conventional methods. The key steps are preprocessing images, segmenting into characters, extracting 54 features from each character by moving along diagonals in a grid, and training a neural network classifier on the extracted features.
This document presents an offline signature verification system that uses intensity profiles for feature extraction and classification. The system scans signatures, preprocesses them, extracts intensity profiles as features, and trains on mean intensity profiles of genuine signatures. To verify a signature, its intensity profile is extracted and compared to trained profiles. The difference between profiles is analyzed to classify signatures as genuine or forged, aiming for low false acceptance and rejection rates.
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.
This document describes an artificial neural network based offline signature recognition system that uses local texture features. It begins with an introduction to signature recognition and motivation for the system. The system objectives are to develop preprocessing, feature extraction, and recognition phases. In preprocessing, signatures are converted to grayscale, binary, noise is reduced, and images are thinned and resized. Feature extraction extracts texture features like entropy, homogeneity, contrast, correlation and energy. Recognition is done using an artificial neural network classifier that compares test signature features to trained features. The system was tested on a database of 95 individuals with 10 signatures each, achieving 85-90% identification accuracy. Local texture features and neural network classification provide an effective approach to offline signature recognition.
An offline signature verification using pixels intensity levelsSalam Shah
Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used.
For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This slide proposed a method to authenticate a signature in off-line. Our proposed method uses "Harris Corner Detector", "Orientation Assignment" , "KNN Classifier", "Hungarian Algorithm".
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
OFFLINE SIGNATURE RECOGNITION VIA CONVOLUTIONAL NEURAL NETWORK AND MULTIPLE C...IJNSA Journal
The document describes a study that used a convolutional neural network (CNN) and three classifiers - support vector machine (SVM), k-nearest neighbors (KNN), and naive Bayes (NB) - to recognize offline signatures. A pre-trained CNN was used to extract features from signature images, which were then classified using the three algorithms. The SVM, KNN, and NB classifiers were compared based on run time, classification error, classification loss, and accuracy. The results showed that the SVM and KNN classifiers achieved the best accuracy of 76.21% and had faster run times and lower error rates than the NB classifier. Therefore, the SVM performed the best overall for offline signature recognition.
Estimation of Age Through Fingerprints Using Wavelet Transform and Singular V...CSCJournals
The forensic investigators always search for fingerprint evidence which is seen as one of the best types of physical evidence linking a suspect to the crime. In this paper discrete wavelet transform (DWT) and the singular value decomposition (SVD) has been used to estimate a person’s age using his/her fingerprint. The most robust K nearest neighbor (KNN) used as a classifier. The evaluation of the system is carried on using internal database of 3570 fingerprints in which 1980 were male fingerprints and 1590 were female fingerprints. Tested fingerprint is grouped into any one of the following five groups: up to 12, 13-19, 20-25, 26-35 and 36 and above. By the proposed method, fingerprints were classified accurately by 96.67%, 71.75%, 86.26%, 76.39% and 53.14% in five groups respectively for male and similarly classified by 66.67%, 63.64%, 76.77%, 72.41% and 16.79% in five groups respectively for female.
Fraud Detection Using Signature RecognitionTejraj Thakor
The signature of person is an important bio metric of a human being which can be used to authenticate human identity. The problem arises when someone decide to imitate our signature and steal our identity.
The Image of human signature is collected by camera of mobile phone which can extract dynamic and spatial information of the signature based on Image processing techniques like Convert to gray scale, Noise Removal, Normalization, Border Elimination and Feature Extraction techniques.
The signature matching is depending on SVM. The SVM classifier is trained with sample images in database obtained from those individuals whose signatures have to be authenticated by the system. In our proposed system SQLite database as a back-end and Android platform as a front-end.
This document proposes a grid-based feature extraction method for offline signature verification. It begins with an introduction and discusses existing techniques and their limitations. It then presents the proposed work, which involves signature acquisition, preprocessing, feature extraction by segmenting the signature image into a grid, and verification. The algorithms, mathematical model, advantages and applications are described. The document concludes that the proposed method requires only low-cost hardware and has a low error rate for signature verification.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
This document discusses a face recognition system that uses Gabor feature extraction and neural networks. 40 Gabor filters are applied to images to extract features with different orientations. Fiducial points are identified based on maximum intensity points and distances between points are calculated. These distances are compared to a pre-defined database to recognize faces. A neural network with multiple layers is used to classify faces based on the Gabor filter outputs. The system was able to accurately detect faces in test images by comparing distances between fiducial points to the stored database.
Measuring memetic algorithm performance on image fingerprints datasetTELKOMNIKA JOURNAL
Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time.
This document presents a proposed methodology for offline signature recognition using global and grid features extracted from signature images. The methodology involves preprocessing signatures, extracting global and grid features using discrete wavelet transforms, training a backpropagation neural network on the features, and classifying signatures based on the trained network. Experimental results show classification accuracy rates ranging from 89-93% for signatures from 10 to 50 individuals. Future work could involve exploring different signature features to potentially improve recognition performance.
FEATURE EXTRACTION METHODS FOR IRIS RECOGNITION SYSTEM: A SURVEYijcsit
This document summarizes several feature extraction methods for iris recognition systems. It discusses supervised, unsupervised, and semi-supervised learning approaches for iris recognition. It also reviews related literature on iris recognition techniques, including using wavelet transforms, SVM classifiers, and other feature extraction methods. Tables in the document compare different biometric traits and traditional biometric systems, as well as summarize reviewed articles on iris recognition with their main contributions. The methodology section describes the typical four steps of an iris recognition system: image acquisition, preprocessing, feature extraction, and matching/recognition. It also discusses various iris recognition methods and their performance measures.
Handwritten Signature Verification using Artificial Neural NetworkEditor IJMTER
This paper reviews various Signature Verification approaches; various feature sets,
various online databases and types of features. Processing on an online database, post extracting a
combination of global and local features onto a signature as an image, using MultiLayer Perceptron Feed
Forward Network alongwith Back Propogation Algorithm for training is proposed to classify a genuine
and forged (random, simple and skilled) offline signatures.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
Fingerprint Based Gender Classification by using Fuzzy C- Means and Neural Ne...IRJET Journal
The document presents a method for classifying gender using fingerprint images. It proposes extracting features from fingerprints like ridge count and thickness using Gabor filters and binarization. A fuzzy C-means algorithm and neural network are used to classify gender based on the extracted features. The method is evaluated on a dataset of 400 fingerprints (200 male, 200 female) and achieves over 90% accuracy in classifying gender. It concludes there is still room for improvement by exploring additional feature extraction and classification techniques to increase accuracy of fingerprint-based gender classification.
Enhanced Latent Fingerprint Segmentation through Dictionary Based ApproachEditor IJMTER
The accuracy of latent finger print matching compared to roll and plain finger print
matching is significantly lower due to background noise, poor ridge quality and overlapping
structured noise in latent images. In this paper the proposed algorithm is dictionary-based approach
for automatic segmentation and enhancement towards the goal of achieving “lights out” latent
identifications system. Total variation decomposition model with L1 fidelity regularization in latent
finger print image remove background noise. A coarse to fine strategy is used to improve robustness
and accuracy. It improves the computational efficiency of the algorithm.
Cursive Handwriting Recognition System using Feature Extraction and Artif...IRJET Journal
The document describes a system for recognizing cursive handwriting using feature extraction and an artificial neural network. It involves preprocessing scanned images, segmenting them into individual characters, extracting features from the characters using a diagonal scanning method, and classifying the characters using a neural network. This approach provides higher recognition accuracy compared to conventional methods. The key steps are preprocessing images, segmenting into characters, extracting 54 features from each character by moving along diagonals in a grid, and training a neural network classifier on the extracted features.
This document presents an offline signature verification system that uses intensity profiles for feature extraction and classification. The system scans signatures, preprocesses them, extracts intensity profiles as features, and trains on mean intensity profiles of genuine signatures. To verify a signature, its intensity profile is extracted and compared to trained profiles. The difference between profiles is analyzed to classify signatures as genuine or forged, aiming for low false acceptance and rejection rates.
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.
This document describes an artificial neural network based offline signature recognition system that uses local texture features. It begins with an introduction to signature recognition and motivation for the system. The system objectives are to develop preprocessing, feature extraction, and recognition phases. In preprocessing, signatures are converted to grayscale, binary, noise is reduced, and images are thinned and resized. Feature extraction extracts texture features like entropy, homogeneity, contrast, correlation and energy. Recognition is done using an artificial neural network classifier that compares test signature features to trained features. The system was tested on a database of 95 individuals with 10 signatures each, achieving 85-90% identification accuracy. Local texture features and neural network classification provide an effective approach to offline signature recognition.
An offline signature verification using pixels intensity levelsSalam Shah
Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used.
For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This slide proposed a method to authenticate a signature in off-line. Our proposed method uses "Harris Corner Detector", "Orientation Assignment" , "KNN Classifier", "Hungarian Algorithm".
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
OFFLINE SIGNATURE RECOGNITION VIA CONVOLUTIONAL NEURAL NETWORK AND MULTIPLE C...IJNSA Journal
The document describes a study that used a convolutional neural network (CNN) and three classifiers - support vector machine (SVM), k-nearest neighbors (KNN), and naive Bayes (NB) - to recognize offline signatures. A pre-trained CNN was used to extract features from signature images, which were then classified using the three algorithms. The SVM, KNN, and NB classifiers were compared based on run time, classification error, classification loss, and accuracy. The results showed that the SVM and KNN classifiers achieved the best accuracy of 76.21% and had faster run times and lower error rates than the NB classifier. Therefore, the SVM performed the best overall for offline signature recognition.
Estimation of Age Through Fingerprints Using Wavelet Transform and Singular V...CSCJournals
The forensic investigators always search for fingerprint evidence which is seen as one of the best types of physical evidence linking a suspect to the crime. In this paper discrete wavelet transform (DWT) and the singular value decomposition (SVD) has been used to estimate a person’s age using his/her fingerprint. The most robust K nearest neighbor (KNN) used as a classifier. The evaluation of the system is carried on using internal database of 3570 fingerprints in which 1980 were male fingerprints and 1590 were female fingerprints. Tested fingerprint is grouped into any one of the following five groups: up to 12, 13-19, 20-25, 26-35 and 36 and above. By the proposed method, fingerprints were classified accurately by 96.67%, 71.75%, 86.26%, 76.39% and 53.14% in five groups respectively for male and similarly classified by 66.67%, 63.64%, 76.77%, 72.41% and 16.79% in five groups respectively for female.
Fraud Detection Using Signature RecognitionTejraj Thakor
The signature of person is an important bio metric of a human being which can be used to authenticate human identity. The problem arises when someone decide to imitate our signature and steal our identity.
The Image of human signature is collected by camera of mobile phone which can extract dynamic and spatial information of the signature based on Image processing techniques like Convert to gray scale, Noise Removal, Normalization, Border Elimination and Feature Extraction techniques.
The signature matching is depending on SVM. The SVM classifier is trained with sample images in database obtained from those individuals whose signatures have to be authenticated by the system. In our proposed system SQLite database as a back-end and Android platform as a front-end.
This document proposes a grid-based feature extraction method for offline signature verification. It begins with an introduction and discusses existing techniques and their limitations. It then presents the proposed work, which involves signature acquisition, preprocessing, feature extraction by segmenting the signature image into a grid, and verification. The algorithms, mathematical model, advantages and applications are described. The document concludes that the proposed method requires only low-cost hardware and has a low error rate for signature verification.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
This document discusses a face recognition system that uses Gabor feature extraction and neural networks. 40 Gabor filters are applied to images to extract features with different orientations. Fiducial points are identified based on maximum intensity points and distances between points are calculated. These distances are compared to a pre-defined database to recognize faces. A neural network with multiple layers is used to classify faces based on the Gabor filter outputs. The system was able to accurately detect faces in test images by comparing distances between fiducial points to the stored database.
Measuring memetic algorithm performance on image fingerprints datasetTELKOMNIKA JOURNAL
Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time.
This document presents a proposed methodology for offline signature recognition using global and grid features extracted from signature images. The methodology involves preprocessing signatures, extracting global and grid features using discrete wavelet transforms, training a backpropagation neural network on the features, and classifying signatures based on the trained network. Experimental results show classification accuracy rates ranging from 89-93% for signatures from 10 to 50 individuals. Future work could involve exploring different signature features to potentially improve recognition performance.
FEATURE EXTRACTION METHODS FOR IRIS RECOGNITION SYSTEM: A SURVEYijcsit
This document summarizes several feature extraction methods for iris recognition systems. It discusses supervised, unsupervised, and semi-supervised learning approaches for iris recognition. It also reviews related literature on iris recognition techniques, including using wavelet transforms, SVM classifiers, and other feature extraction methods. Tables in the document compare different biometric traits and traditional biometric systems, as well as summarize reviewed articles on iris recognition with their main contributions. The methodology section describes the typical four steps of an iris recognition system: image acquisition, preprocessing, feature extraction, and matching/recognition. It also discusses various iris recognition methods and their performance measures.
Handwritten Signature Verification using Artificial Neural NetworkEditor IJMTER
This paper reviews various Signature Verification approaches; various feature sets,
various online databases and types of features. Processing on an online database, post extracting a
combination of global and local features onto a signature as an image, using MultiLayer Perceptron Feed
Forward Network alongwith Back Propogation Algorithm for training is proposed to classify a genuine
and forged (random, simple and skilled) offline signatures.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
Fingerprint Based Gender Classification by using Fuzzy C- Means and Neural Ne...IRJET Journal
The document presents a method for classifying gender using fingerprint images. It proposes extracting features from fingerprints like ridge count and thickness using Gabor filters and binarization. A fuzzy C-means algorithm and neural network are used to classify gender based on the extracted features. The method is evaluated on a dataset of 400 fingerprints (200 male, 200 female) and achieves over 90% accuracy in classifying gender. It concludes there is still room for improvement by exploring additional feature extraction and classification techniques to increase accuracy of fingerprint-based gender classification.
Enhanced Latent Fingerprint Segmentation through Dictionary Based ApproachEditor IJMTER
The accuracy of latent finger print matching compared to roll and plain finger print
matching is significantly lower due to background noise, poor ridge quality and overlapping
structured noise in latent images. In this paper the proposed algorithm is dictionary-based approach
for automatic segmentation and enhancement towards the goal of achieving “lights out” latent
identifications system. Total variation decomposition model with L1 fidelity regularization in latent
finger print image remove background noise. A coarse to fine strategy is used to improve robustness
and accuracy. It improves the computational efficiency of the algorithm.
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.
A Survey on Fingerprint Identification for Different Orientation Images.IRJET Journal
This document summarizes several papers on fingerprint identification techniques for images with different orientations. It discusses methods like discrete wavelet transform, Gabor filters, dual tree complex wavelet transform, and backpropagation neural networks. Evaluation of these techniques show they can extract features from low quality and rotated images but may struggle with large datasets or high levels of distortion. The document concludes different applications could select techniques or their combination based on required accuracy and robustness to orientation.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
A REVIEW ON LATENT FINGERPRINT RECONSTRUCTION METHODSIRJET Journal
This document reviews several methods for reconstructing latent fingerprints from minutiae points. It begins with an introduction to fingerprint features and representation. It then summarizes 10 research papers on latent fingerprint reconstruction methods. These include approaches using deep learning networks, fusion of minutiae and pore features, progressive feedback mechanisms, orientation field and phase reconstruction, and other techniques. The document concludes that while reconstruction methods have improved, there remains a performance gap when matching reconstructed prints to originals. The purpose is to provide a comparative analysis of existing latent fingerprint reconstruction methods.
SIGN LANGUAGE RECOGNITION USING MACHINE LEARNINGIRJET Journal
1. The document describes a study on developing a real-time sign language recognition system using machine learning. The system captures hand gestures using a webcam and identifies the region of interest to predict the sign.
2. Convolutional neural networks are used to train the model to classify signs. Related works that also use CNNs and other machine learning techniques for sign language recognition from images are discussed.
3. The proposed system aims to make communication easier for deaf and mute people by automatically translating signs to text in real-time without requiring an expert translator.
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.
Comparative study of various enhancement techniques for finger print imagesMade Artha
The document discusses various techniques for enhancing fingerprint images. It begins by explaining how fingerprints are used for biometric identification but that fingerprint images are often degraded, requiring enhancement techniques prior to minutiae extraction. The purpose of the study is to implement and evaluate different enhancement techniques on synthetic and real fingerprint images. It discusses how enhancement improves image quality and reliability of minutiae extraction, which is important for fingerprint-based identification and verification applications. The document also provides details on enrollment and identification processes using fingerprint biometrics.
Comparative study of various enhancement techniques for finger print imagesMade Artha
The document discusses various techniques for enhancing fingerprint images. It begins by stating that fingerprints are widely used for biometric identification but can be degraded in quality. Various enhancement techniques are employed to improve image quality and enable more reliable minutiae extraction. The document evaluates different enhancement techniques on synthetic and real fingerprint images. It finds that combining wavelet filtering and anisotropic filtering effectively enhances low-quality fingerprints while preserving ridge structure details.
FINGERPRINT MATCHING USING HYBRID SHAPE AND ORIENTATION DESCRIPTOR -AN IMPROV...IJCI JOURNAL
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process.
Fingerprints are broadly used for personal identification due to its feasibility, distinctiveness, permanence,
accuracy and acceptability. This paper proposes a way to improve the Equal Error Rate (EER) in
fingerprint matching techniques in the domain of hybrid shape and orientation descriptor. This type of
fingerprint matching domain is popular due to capability of filtering false and strange minutiae pairings.
EER is calculated by using FMR and FNMR to check the performance of proposed technique.
An Indexing Technique Based on Feature Level Fusion of Fingerprint FeaturesIDES Editor
Personal identification system based on pass word
and other entities are ineffective. Nowadays biometric based
systems are used for human identification in almost many
real time applications. The current state-of-art biometric
identification focuses on accuracy and hence a good
performance result in terms of response time on small scale
database is achieved. But in today’s real life scenario biometric
database are huge and without any intelligent scheme the
response time should be high, but the existing algorithms
requires an exhaustive search on the database which increases
proportionally when the size of the database grows. This paper
addresses the problem of biometric indexing in the context of
fingerprint. Indexing is a technique to reduce the number of
candidate identities to be considered by the identification
algorithm. The fingerprint indexing methodology projected
in this work is based on a combination of Level 1, Level 2 and
Level-3 fingerprint features. The result shows the fusion of
level 1, level 2 and level 3 features gives better performance
and good indexing rate than with any one level of fingerprint
feature.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
This document summarizes a research paper that proposes an efficient method for recognizing low quality fingerprints using cross correlation. It begins with an introduction to fingerprint identification and verification. It then describes the proposed system, which uses orientation field methodology as a preprocessing step to convert images to orientation patterns. The input image is cross correlated with images in a cluster, and the highest correlated image is output. Experimental results on 1000 fingerprints from a public database showed the method achieved an 85% recognition rate. The paper concludes the cross correlation of orientation fields is an effective approach for fingerprint identification, especially for low quality images.
IMPROVEMENT OF THE FINGERPRINT RECOGNITION PROCESSADEIJ Journal
The increased development of IT tools and social communication networks has significantly increased the
need for people to be identified with reliable and secure tools hence the importance of using biometric
technology. Biometrics is an emerging field where technology improves our ability to identify a person. The
advantage of biometric identification is that each individual has its own physical characteristics that
cannot be changed, lost or stolen. The use of fingerprinting is today one of the most reliable technologies
on the market to authenticate an individual. This technology is simple to use and easy to implement. The
techniques of fingerprint recognition are numerous and diversified, they are generally based on generic
algorithms and tools for filtering images.
Improvement of the Fingerprint Recognition Processijbbjournal
This document proposes improvements to fingerprint recognition processes. It summarizes a fingerprint recognition algorithm that includes pre-processing the fingerprint image using techniques like grayscale transformation, normalization, segmentation, and Gabor filtering to improve image quality. It then extracts biometric data by binarizing the image, skeletonizing it, and detecting minutiae points using the Crossing Number method. The algorithm is validated using Matlab to retrieve and analyze results.
This document summarizes a research paper that proposes a feature level fusion based bimodal biometric authentication system using fingerprint and face recognition with transformation domain techniques. The system extracts fingerprint features using Dual Tree Complex Wavelet Transforms and extracts face features using Discrete Wavelet Transforms. It then concatenates the fingerprint and face features into a single feature vector. Euclidean distance is used to match test biometrics to those stored in a database. The proposed algorithm is shown to achieve better equal error rates and true positive identification rates compared to individual transformation domain techniques.
This document describes a new methodology for improving the accuracy of fingerprint verification systems. It proposes detecting singular points like core and delta points, and indexing templates based on the occurrence of delta points relative to the core point. Experiments on the FVC2006 database show the proposed method achieves higher recognition rates and lower false acceptance and rejection rates compared to existing minutiae-based matching techniques, especially for distorted images. It provides a concise way to represent templates and allows for faster matching by first comparing singular point information before minutiae points.
This document describes a new methodology for improving the accuracy of fingerprint verification systems. It proposes detecting singular points like core and delta points, and indexing templates based on the occurrence of delta points relative to the core point. Experiments on the FVC2006 database show the proposed method achieves higher recognition rates and lower false acceptance and rejection rates compared to existing minutiae-based matching techniques, especially for distorted images. It introduces a new way of storing templates as strings of numbers that encode singular point and minutiae information to enable faster matching.
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