This document presents a multimodal biometric system for identifying identical twins using face, fingerprint, and iris recognition. It utilizes Fisher's linear discriminant analysis to extract features from faces, principal component analysis for fingerprints, and local binary pattern features for iris matching. These features are then fused for identification. The system is tested on a database of 50 pairs of identical twins and shows promising results compared to other techniques. Receiver operating characteristics also indicate the proposed method performs better than other studied techniques in distinguishing identical twins.
The document discusses multispectral palm image fusion for biometric authentication using ant colony optimization. It introduces intra-modal fusion of palmprint images from multiple spectra to improve accuracy. The key steps involve detecting the region of interest, fusing the images using wavelet transforms, extracting Gabor features, selecting optimal features using ant colony optimization, and classifying with support vector machines. Experimental results and conclusions are also presented.
This document discusses multimodal biometric systems, which use multiple physical or behavioral traits to authenticate a person's identity. It provides background on unimodal biometric systems and their limitations. A multimodal system combines results from multiple traits to be more accurate and secure than a unimodal system. The document describes the main components of a biometric system (sensor, feature extraction, matching, decision) and discusses different levels at which traits can be fused in a multimodal system (sensor, feature, matching score, and decision levels). The goal is to highlight how multimodal biometrics provides higher identification performance than unimodal biometrics alone.
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
This document presents a novel method called QPLC for multimodal biometric score fusion. QPLC performs quantile power transformation (QPT) on scores from different biometric classifiers to better separate genuine and imposter score distributions. It then uses linear classifiers like logistic regression to fuse the transformed scores. The authors test QPLC on a public NIST dataset and show it outperforms other published score fusion methods, achieving higher true accept rates for a given low false accept rate. They also apply QPT to transform scores before training a linear support vector machine (SVM) classifier, finding it improves SVM performance over using original scores.
This document discusses two techniques for finger knuckle print recognition: Gabor filtering and Dual Tree Complex Wavelet Transform (DT-CWT). Gabor filtering is applied to extract spatial-frequency and orientation information from finger knuckle print images. DT-CWT is also used for feature extraction and is found to provide more discriminative features while being less computationally complex than Gabor filtering. The document analyzes the PolyU FKP database of 7920 images using both techniques and compares their performance based on metrics like false acceptance rate, true acceptance rate, and false rejection rate to evaluate the pros and cons of each approach.
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. There have been several attempts to perform
automatic background identification. This paper reviews state-of-the-art automatic bacteria identification
techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria
identification systems and recommends future direction of automatic bacteria identification.
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.
Robust Analysis of Multibiometric Fusion Versus Ensemble Learning Schemes: A ...CSCJournals
Identification of person using multiple biometric is very common approach used in existing user
validation of systems. Most of multibiometric system depends on fusion schemes, as much of the
fusion techniques have shown promising results in literature, due to the fact of combining multiple
biometric modalities with suitable fusion schemes. However, similar type of practices are found in
ensemble of classifiers, which increases the classification accuracy while combining different
types of classifiers. In this paper, we have evaluated comparative study of traditional fusion
methods like feature level and score level fusion with the well-known ensemble methods such as
bagging and boosting. Precisely, for our frame work experimentations, we have fused face and
palmprint modalities and we have employed probability model - Naive Bayes (NB), neural
network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support
Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning
ensemble approaches namely, Boosting and Bagging are statistically well recognized. From
experimental results, in biometric fusion the traditional method, score level fusion is highly
recommended strategy than ensemble learning techniques.
The document discusses multispectral palm image fusion for biometric authentication using ant colony optimization. It introduces intra-modal fusion of palmprint images from multiple spectra to improve accuracy. The key steps involve detecting the region of interest, fusing the images using wavelet transforms, extracting Gabor features, selecting optimal features using ant colony optimization, and classifying with support vector machines. Experimental results and conclusions are also presented.
This document discusses multimodal biometric systems, which use multiple physical or behavioral traits to authenticate a person's identity. It provides background on unimodal biometric systems and their limitations. A multimodal system combines results from multiple traits to be more accurate and secure than a unimodal system. The document describes the main components of a biometric system (sensor, feature extraction, matching, decision) and discusses different levels at which traits can be fused in a multimodal system (sensor, feature, matching score, and decision levels). The goal is to highlight how multimodal biometrics provides higher identification performance than unimodal biometrics alone.
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
This document presents a novel method called QPLC for multimodal biometric score fusion. QPLC performs quantile power transformation (QPT) on scores from different biometric classifiers to better separate genuine and imposter score distributions. It then uses linear classifiers like logistic regression to fuse the transformed scores. The authors test QPLC on a public NIST dataset and show it outperforms other published score fusion methods, achieving higher true accept rates for a given low false accept rate. They also apply QPT to transform scores before training a linear support vector machine (SVM) classifier, finding it improves SVM performance over using original scores.
This document discusses two techniques for finger knuckle print recognition: Gabor filtering and Dual Tree Complex Wavelet Transform (DT-CWT). Gabor filtering is applied to extract spatial-frequency and orientation information from finger knuckle print images. DT-CWT is also used for feature extraction and is found to provide more discriminative features while being less computationally complex than Gabor filtering. The document analyzes the PolyU FKP database of 7920 images using both techniques and compares their performance based on metrics like false acceptance rate, true acceptance rate, and false rejection rate to evaluate the pros and cons of each approach.
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. There have been several attempts to perform
automatic background identification. This paper reviews state-of-the-art automatic bacteria identification
techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria
identification systems and recommends future direction of automatic bacteria identification.
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.
Robust Analysis of Multibiometric Fusion Versus Ensemble Learning Schemes: A ...CSCJournals
Identification of person using multiple biometric is very common approach used in existing user
validation of systems. Most of multibiometric system depends on fusion schemes, as much of the
fusion techniques have shown promising results in literature, due to the fact of combining multiple
biometric modalities with suitable fusion schemes. However, similar type of practices are found in
ensemble of classifiers, which increases the classification accuracy while combining different
types of classifiers. In this paper, we have evaluated comparative study of traditional fusion
methods like feature level and score level fusion with the well-known ensemble methods such as
bagging and boosting. Precisely, for our frame work experimentations, we have fused face and
palmprint modalities and we have employed probability model - Naive Bayes (NB), neural
network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support
Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning
ensemble approaches namely, Boosting and Bagging are statistically well recognized. From
experimental results, in biometric fusion the traditional method, score level fusion is highly
recommended strategy than ensemble learning techniques.
Face recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
This document presents a bimodal biometric system that fuses fingerprint and iris features for identification. It extracts features from the iris using two-level discrete wavelet transformation and discrete cosine transformation. Fingerprint features are extracted using fast Fourier transformation and discrete wavelet transformation. The iris and fingerprint features are concatenated to form the final feature set. Experimental results on fingerprint and iris databases show that the proposed bimodal system has lower false rejection and false acceptance rates and higher total success rate compared to existing unimodal systems.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
This document presents an efficient iris recognition system using wavelet transform that achieves a small template size of 364 bits. The system first segments the iris from eye images and applies normalization to account for variations in scale. It then proposes a method to remove the eyelid and eyelash regions by masking parts of the iris. Next, image enhancement increases contrast before feature extraction using wavelet transform. The document evaluates different combinations of wavelet coefficients and quantization levels to determine the most effective approach. Experimental results demonstrate false accept and reject rates of 0% and 1% respectively.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
The document describes a proposed algorithm called Fusion of Hybrid Domain features for Iris Recognition (FHDIR).
The algorithm pre-processes iris images by resizing, binarization, cropping and splitting them. It then applies Fast Fourier Transform (FFT) to the left half of the iris image to extract features and applies Principal Component Analysis (PCA) to the right half to extract features. These feature sets are then fused using arithmetic addition to generate a final feature vector. Test iris features are compared to the database using Euclidean Distance for identification.
The proposed algorithm is evaluated on the CASIA iris database and is found to have better performance than existing algorithms in terms of false rejection rate, false acceptance rate, and true
Extended Fuzzy Hyperline Segment Neural Network for Fingerprint RecognitionCSCJournals
In this paper we have proposed Extended Fuzzy Hyperline Segment Neural Network (EFHLSNN) and its learning algorithm which is an extension of Fuzzy Hyperline Segment Neural Network (FHLSNN). The fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding extended membership function. The fingerprint feature extraction process is based on FingerCode feature extraction technique. The performance of EFHLSNN is verified using POLY U HRF fingerprint database. The EFHLSNN is found superior compared to FHLSNN in generalization, training and recall time.
This document presents a new iris segmentation method for iris recognition systems. The proposed method uses Canny edge detection and Hough transform to locate the iris boundary after finding the pupil boundary using image gray levels. Experiments on the CASIA iris image database of 756 images show the method can accurately detect the iris boundary in 99.2% of images. This is an improvement over other existing segmentation techniques. The key steps of the proposed method are preprocessing, segmentation using Canny edge detection and Hough transform, normalization using the rubber sheet model, feature encoding with Gabor wavelets, and matching with Hamming distance.
Iris recognition is a method of biometric identification.
Biometric identification provides automatic recognition of an
individual based on the unique feature of physiological
characteristics or behavioral characteristic. Iris recognition is a
method of recognizing a person by analyzing the iris pattern.
This survey paper covers the different iris recognition techniques
and methods.
The Biometric Algorithm based on Fusion of DWT Frequency Components of Enhanc...CSCJournals
The biometrics are used to authenticate a person effectively compared to conventional methods of identification. In this paper we propose the biometric algorithm based on fusion of Discrete Wavelet Transform(DWT) frequency components of enhanced iris image.The iris template is extracted from an eye image by considering horizontal pixels in an iris part.The iris template contrast is enhanced using Adaptive Histogram Equalization (AHE) and Histogram Equalization (HE).The DWT is applied on enhanced iris template.The features are formed by straight line fusion of low and high frequency coefficients of DWT.The Euclidian distance is used to compare final test features with database features. It is observed that the performance parameters are better in the case of proposed algorithm compared to existing algorithms.
The document describes a multimodal biometric identification system based on iris and fingerprint recognition. It discusses the individual steps of iris and fingerprint recognition including segmentation, feature extraction, and matching. For fingerprint recognition, minutiae points are extracted and matched. For iris recognition, the iris region is segmented from an eye image and normalized before feature extraction. The multimodal system fuses the matching scores from the individual iris and fingerprint recognition at the matching level to improve accuracy by reducing false acceptance and rejection rates compared to unimodal systems. Experimental results on standard databases show the proposed multimodal approach achieves better performance than unimodal biometrics.
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
Visual character n grams for classification and retrieval of radiological imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram
features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases.
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.
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the
security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed
systems, make it a good candidate to replace most of thesecurity systems around. By making use of the
distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person.
Identification of this person is possible by applying appropriate matching algorithm.In this paper,
Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical
analysis of different feature detection operators is performed, features extracted is encoded using Haar
wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on
the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of
the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and
False Reject Rate is 10%.
This document summarizes research on combining face and fingerprint biometrics at the data level using fusion. Multimodal biometric systems that combine evidence from multiple sources can improve recognition accuracy, population coverage, and fault tolerance compared to unimodal systems. The document discusses related work on multimodal biometrics fusion and approaches like DWT coefficient selection. It also outlines the proposed approach of fusing face and fingerprint images using wavelet transform for feature extraction and similarity measures for the multimodal biometric system.
MSB based Face Recognition Using Compression and Dual Matching TechniquesCSCJournals
Biometrics are used in almost all communication technology applications for secure recognition. In this paper, we propose MSB based face recognition using compression and dual matching techniques. The standard available face images are considered to test the proposed method. The novel concept of considering only four Most Significant Bits (MSB) of each pixel on image is introduced to reduce the total number of bits to half of an image for high speed computation and less architectural complexity. The Discrete Wavelet Transform (DWT) is applied to an image with only MSB's, and consider only LL band coefficients as final features. The features of the database and test images are compared using Euclidian Distance (ED) an Artificial Neural Network (ANN) to test the performance of the pot method. It is observed that, the performance of the proposed method is better than the existing methods.
This document introduces the Great Gumby Project, a 2-week challenge to get up from your desk at least twice a day to stretch muscles that become tight from sitting all day. It lists various stretches targeting areas like the calf, chest, wrists, shoulders, neck, hips and back. The stretches are described along with the reasons they are important, such as countering the effects of wearing heels or sitting hunched over a keyboard. Participants are encouraged to do each stretch for 30 seconds and congratulate themselves for taking part in improving their health and posture.
Fostering a Diversity Culture in Business: Npower and the Recruitment of Ethn...CSCJournals
This research delves into a multi layered reality of Diversity in recruitment from NPower. The purpose of this small scale research was to evaluate the practice of diversity in recruitment in the organization to attempt answering wider questions regarding the importance of diversity in business and the challenges in its practical implementation. Although there is a vast amount of literature and research on the sociologically, ethically and economically important subject of Diversity; this research develops the idea further by delving into the organizational practice of the policy, evaluating the problems and challenges in its practical implementation. In order to answer the questions raised by this study, the researcher relied on semi-structured in-depth interviews. Data generated by these semi structured interviews formed the basis for theorising in this inductive research. In addition, statistical data of employee composition by ethnicity was presented to supplement the information provided by the interviews. Having made best use of the resources available and worked within the constraints of a small scale academic project, the researcher asserts that this research, although by no means the final word on this subject, can be a reference or a starting point for further research in the field.
The number of food recalls has nearly doubled since 2004, often costing companies over $10 million per recall. Denham Plastics is a packaging consultant that offers sustainable and reusable packaging to help mitigate risks of foreign objects and foodborne illness. They support customers with packaging services including repair, recycling, washing and have over 60 years of experience developing food-safe packaging.
Face recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
This document presents a bimodal biometric system that fuses fingerprint and iris features for identification. It extracts features from the iris using two-level discrete wavelet transformation and discrete cosine transformation. Fingerprint features are extracted using fast Fourier transformation and discrete wavelet transformation. The iris and fingerprint features are concatenated to form the final feature set. Experimental results on fingerprint and iris databases show that the proposed bimodal system has lower false rejection and false acceptance rates and higher total success rate compared to existing unimodal systems.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
This document presents an efficient iris recognition system using wavelet transform that achieves a small template size of 364 bits. The system first segments the iris from eye images and applies normalization to account for variations in scale. It then proposes a method to remove the eyelid and eyelash regions by masking parts of the iris. Next, image enhancement increases contrast before feature extraction using wavelet transform. The document evaluates different combinations of wavelet coefficients and quantization levels to determine the most effective approach. Experimental results demonstrate false accept and reject rates of 0% and 1% respectively.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
The document describes a proposed algorithm called Fusion of Hybrid Domain features for Iris Recognition (FHDIR).
The algorithm pre-processes iris images by resizing, binarization, cropping and splitting them. It then applies Fast Fourier Transform (FFT) to the left half of the iris image to extract features and applies Principal Component Analysis (PCA) to the right half to extract features. These feature sets are then fused using arithmetic addition to generate a final feature vector. Test iris features are compared to the database using Euclidean Distance for identification.
The proposed algorithm is evaluated on the CASIA iris database and is found to have better performance than existing algorithms in terms of false rejection rate, false acceptance rate, and true
Extended Fuzzy Hyperline Segment Neural Network for Fingerprint RecognitionCSCJournals
In this paper we have proposed Extended Fuzzy Hyperline Segment Neural Network (EFHLSNN) and its learning algorithm which is an extension of Fuzzy Hyperline Segment Neural Network (FHLSNN). The fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding extended membership function. The fingerprint feature extraction process is based on FingerCode feature extraction technique. The performance of EFHLSNN is verified using POLY U HRF fingerprint database. The EFHLSNN is found superior compared to FHLSNN in generalization, training and recall time.
This document presents a new iris segmentation method for iris recognition systems. The proposed method uses Canny edge detection and Hough transform to locate the iris boundary after finding the pupil boundary using image gray levels. Experiments on the CASIA iris image database of 756 images show the method can accurately detect the iris boundary in 99.2% of images. This is an improvement over other existing segmentation techniques. The key steps of the proposed method are preprocessing, segmentation using Canny edge detection and Hough transform, normalization using the rubber sheet model, feature encoding with Gabor wavelets, and matching with Hamming distance.
Iris recognition is a method of biometric identification.
Biometric identification provides automatic recognition of an
individual based on the unique feature of physiological
characteristics or behavioral characteristic. Iris recognition is a
method of recognizing a person by analyzing the iris pattern.
This survey paper covers the different iris recognition techniques
and methods.
The Biometric Algorithm based on Fusion of DWT Frequency Components of Enhanc...CSCJournals
The biometrics are used to authenticate a person effectively compared to conventional methods of identification. In this paper we propose the biometric algorithm based on fusion of Discrete Wavelet Transform(DWT) frequency components of enhanced iris image.The iris template is extracted from an eye image by considering horizontal pixels in an iris part.The iris template contrast is enhanced using Adaptive Histogram Equalization (AHE) and Histogram Equalization (HE).The DWT is applied on enhanced iris template.The features are formed by straight line fusion of low and high frequency coefficients of DWT.The Euclidian distance is used to compare final test features with database features. It is observed that the performance parameters are better in the case of proposed algorithm compared to existing algorithms.
The document describes a multimodal biometric identification system based on iris and fingerprint recognition. It discusses the individual steps of iris and fingerprint recognition including segmentation, feature extraction, and matching. For fingerprint recognition, minutiae points are extracted and matched. For iris recognition, the iris region is segmented from an eye image and normalized before feature extraction. The multimodal system fuses the matching scores from the individual iris and fingerprint recognition at the matching level to improve accuracy by reducing false acceptance and rejection rates compared to unimodal systems. Experimental results on standard databases show the proposed multimodal approach achieves better performance than unimodal biometrics.
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
Visual character n grams for classification and retrieval of radiological imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram
features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases.
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.
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the
security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed
systems, make it a good candidate to replace most of thesecurity systems around. By making use of the
distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person.
Identification of this person is possible by applying appropriate matching algorithm.In this paper,
Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical
analysis of different feature detection operators is performed, features extracted is encoded using Haar
wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on
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The number of food recalls has nearly doubled since 2004, often costing companies over $10 million per recall. Denham Plastics is a packaging consultant that offers sustainable and reusable packaging to help mitigate risks of foreign objects and foodborne illness. They support customers with packaging services including repair, recycling, washing and have over 60 years of experience developing food-safe packaging.
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Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
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Biometrics is science of measuring and statistically analyzing biological data. Biometric system
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by an individual. Behavioral biometrics measures characteristics which are acquired naturally
over time. Physical biometrics measures inherent physical characteristics on an individual.
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provided by ocular region have led to exploration of newer traits. Feasibility of periocular
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preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and
classification in the periocular region. This paper investigates the effect of using Lower Central
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AN EXPLORATION OF PERIOCULAR REGION WITH REDUCED REGION FOR AUTHENTICATION : ...cscpconf
Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioral characteristic possessed by an individual. Behavioral biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on an individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of
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We explore the task of recognizing peoples’ identities
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dividuals collected from public Flickr photo albums. With
only about half of the person images containing a frontal
face, the recognition task is very challenging due to the
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performance of DeepFace, which is one of the best face rec-
ognizers as measured on the LFW dataset.
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discrimination. Two different types of thermal features are used. The first type is actually based on the
mean value of the pixels of specific locations on the face. All cases of persons from the used database,
males and females, are correctly distinguished based on this feature. Classification results are verified
using two conventional approaches, namely: a. the simplest possible neural network so that generalization
is achieved along with successful discrimination between all persons and b. the leave-one-out approach to
demonstrate the classification performance on unknown persons using the simplest classifiers possible. The
second type takes advantage of the temperature distribution on the ear of the persons. It is found that for
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GENDER DISCRIMINATION BASED ON THE THERMAL SIGNATURE OF THE FACE AND THE EXTE...sipij
Simple features extracted from the thermal infrared images of the persons' face are proposed for gender discrimination. Two different types of thermal features are used. The first type is actually based on the mean value of the pixels of specific locations on the face. All cases of persons from the used database, males and females, are correctly distinguished based on this feature. Classification results are verified using two conventional approaches, namely: a. the simplest possible neural network so that generalization is achieved along with successful discrimination between all persons and b. the leave-one-out approach to demonstrate the classification performance on unknown persons using the simplest classifiers possible. The
second type takes advantage of the temperature distribution on the ear of the persons. It is found that for the men the cooler region on the ear is larger as percentage compared to that of the women.
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texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand
to develop robust methods to verify facial images when they age. In this paper, a deep learning method
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MultiModal Identification System in Monozygotic Twins
1. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 72
MultiModal Identification System in Monozygotic Twins
Dinakardas C.N. dinakardas@gmail.com
Research Scholar,Nims University,Jaipur
& Associate Professor,Lourdes Matha College of Science & Technology
Trivandrum, 695574, India
Dr.S.Perumal Sankar spsankar2004@yahoo.co.in
Research Guide, Nims University, Jaipur
& Professor,CAPE Institute of Technology
Kanyakumari, 629001, India
Nisha George ddcnisha@gmail.com
Assisstant Professor,Lourdes Matha College of Science & Technology
Trivandrum, 695574, India
Abstract
With the increase in the number of twin births in recent decades, there is a need to develop
alternate approaches that can secure the biometric system. In this paper an effective fusion
scheme is presented that combines information presented by multiple domain experts based on
the rank-level fusion integration method. The developed multimodal biometric system possesses
a number of unique qualities, starting from utilizing Fisher’s Linear Discriminant methods for face
matching, Principal Component Analysis for fingerprint matching and Local binary pattern
features for iris matching and fused the information for effective recognition and authentication
The importance of considering these boundary conditions, such as twins, where the possibility of
errors is maximum will lead us to design a more reliable and robust security system.The
proposed approach is tested on a real database consisting of 50 pair of identical twin images and
shows promising results compared to other techniques. The Receiver Operating Characteristics
also shows that the proposed method is superior compared to other techniques under study
Keywords: Fisher Faces, Principal Component Analysis, Local Binary Pattern Receiver
Operating Characteristics Curve.
1. INTRODUCTION
A biometric identification (matching) system is an automatic pattern recognition system that
recognizes a person by determining the authenticity of a specific physiological and/or behavioral
characteristic (biometric) possessed by that person. Fertility treatments have resulted in an
increase in the identical twin birth rate [1].The identical twin birth rate is about twice as high for
women who use fertility drugs. Identical twins are more precisely described by the term
monozygotic, indicating that they come from the split of a single fertilized embryo thus, they have
the same DNA. Fraternal, or dizygotic, twins are the result of two different fertilized embryos and
have different DNA[2]. Identical twins has been considered to be a problem of only academic
interest but due to consistent increase in twin births in recent decades. The extent of variation in a
physical trait due to random development process differs from trait to trait.
Typically, most of the physical characteristics such as body type, voice, and face are very similar
for identical twins and automatic identification based on face and hand geometry will fail to
distinguish them [3],[4],[5].However, a recent experiment demonstrates that iris biometric
template aging can be detected after as little as two years. A significant number of twin pairs
(206) have been studied for handwriting. These samples were processed with features extracted
2. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 73
and conclusions drawn by comparing verification performances with twins and non-twins. In that
study, the conclusion was that twins are discriminable but less so than an arbitrary pair of
individuals [5],[7].Turk and Pentland popularized the use of PCA for face recognition [8]. They
used PCA to compute a set of subspace basis vectors (which they called “eigenfaces”) for a
database of face images, and projected the images in the database into the compressed
subspace. New test images were then matched to images in the database by projecting them
onto the basis vectors and finding the nearest compressed image in the subspace (eigenspace).
Kong et al. [9] observed that palmprints from identical twins have correlated features.
The same observation was made by Jain et al. [10],[14] for fingerprints also. Srihari et al.[4]
analyzed the similarity between twins fingerprints in a study using fingerprint images from 298
pairs of twins. The authors analyzed this similarity based on the pattern of the ridge flow, and
minutiae. They concluded that the similarity between twin fingers is higher than between two
arbitrary fingers, but twins can still be distinguished using fingerprints .Kocaman et al.[11]
proposed a study on PCA,FLDA,DCVA,and evaluate error and hit rates of four algorithms which
were calculated by random subsampling and k-fold cross validation. . Chang et al.[12] compared
PCA technique for both face and ear images and showed similar performance as biometrics .
Gaurav et al.[16] proposed a method for distinguishing identical twins and the ROC shows the
various comparisons for various set of facial marks in identical twins. Kodate et al. [17]
experimented with 10 sets of identical twins using a 2D face recognition system. Recently, Sun et
al. [6] presented a study of distinctiveness of biometric characteristics in identical twins using
fingerprint, face and iris biometrics. They observed that though iris and fingerprints show little to
no degradation in performance when dealing with identical twins, face matchers find it hard to
distinguish between identical twins. It is believed that the texture of every iris is determined
entirely at random. This implies that the iris textures of two identical twins are no more similar to
each other than the iris textures of unrelated persons. The researchers compared the distribution
of difference values between iris codes from the eyes of identical twins to that between iris codes
of unrelated persons and found that the iris textures of identical twins are no more similar than
those of unrelated persons. Among all biometric traits, the textural structure of the human iris has
been observed to be robust and reliable. However, the performance of iris recognition systems is
adversely affected by the quality of the acquired images.
Today technologies are well-studied, but research shows they have many drawbacks which
decrease the success of the methods applied. The frequently used and most common biological
traits in the field of biometrics are face, finger, and iris. Identifying identical twins is crucial for all
biometric systems. The systems that cannot handle identical twins have a serious security hole.
The rest of this paper is organized as follows. Section 2 describes the Fisher linear Discriminant
analysis, Principal component analysis and Local binary pattern and the results are reported to
evaluate the performance of our proposed approach in Section 3. Finally, conclusions are given in
Section 4.
2. BIMODAL BIOMETRIC SYSTEM DESIGN
In this section deals the development procedures of the proposed multimodal biometric system.
Fisher face features are extracted from the face images and the PCA features from the
fingerprints and the Local Binary pattern based texture pattern from the iris pattern are used for
the enrollment and recognition of biometric traits. This system integrate multiple modalities in
user verification and identification which will lead to higher performance A more detailed
representation of the proposed system is shown in Fig. 1.
3. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 74
FIGURE 1: Block Diagram of the Proposed Bimodal Biometric Identification System.
2.1. Fisher linear discriminant analysis
Fisher linear discriminant analysis (LDA), a widely-used technique for pattern classification, finds a
linear discriminant that yields optimal discrimination between two classes which can be identified
with two random variables, say X and Y in R
n
. PCA in the form of eigen space representation is
very sensitive to image conditions such as background noise, image shift, occlusion of objects,
scaling of the image, and illumination change. When substantial changes in illumination and
expression are present in any image, much of the variation in data is due to these changes [13],
and the eigenimage technique, in this case, cannot give highly reliable results. Due to certain
illumination changes in the face images of the database used in this work, a fisherface based face
recognition method [23] is developed to compare with the eigenface technique. The fisherface
method uses both PCA and LDA to produce a subspace projection matrix, similar to that used in
the eigenface method .The terms Fisher's linear discriminant and LDA are often used
interchangeably, although Fisher's original article actually describes a slightly different
discriminant, which does not make some of the assumptions of LDA such as normally distributed
classes or equal class covariances. Suppose two classes of observations have means,
0=µy
, 1=µy
and covariance
∑ =y
0 , ∑ =y
1
Then the linear combination of features x.ω will have means
iy =µω .
and variances
∑ =y
T
i ωω . , for i = 0,1.
Fisher defined the separation between these two distributions to be the ratio of the variance
between the classes to the variance within the classes as in eq(1)
4. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 75
∑ ∑= =
==
+
−
==
1 0
2
01
2
2
)..(
y y
TT
yy
within
between
S
ωωωω
µωµω
σ
σ
ωω
µµω
)(
))(.(
10
2
01
∑∑ ==
==
+
−
=
yy
T
yy
(1)
This measure is, in some sense, a measure of the signal-to-noise ratio for the class labeling. It
can be shown that the maximum separation occurs when
)()( 01
1
10
==
−
==
−+= ∑∑ yy
yy
µµω
When the assumptions of LDA are satisfied, the above equation is equivalent to LDA. The
vector ω is the normal to the discriminant hyperplane. As an example, in a two dimensional
problem, the line that best divides the two groups is perpendicular to ω .
Generally, the data points to be discriminated are projected onto ω , then the threshold that best
separates the data is chosen from analysis of the one-dimensional distribution. There is no general
rule for the threshold. However, if projections of points from both classes exhibit approximately the
same distributions, the good choice would be hyperplane in the middle between projections of the
two means,
0. =yµω and 1. =yµω .
In this case the parameter c in threshold condition cx <.ω can be found explicitly as in
eq(2):
2/)).(( 10 == += yyc µµω (2).
2.2. Principal Component Analysis
Principal Components Analysis is a method that reduces data dimensionality by performing a
covariance analysis between factors. As such, it is suitable for data sets in multiple dimensions,
such as a large experiment involving huge amount of data. PCA is an unsupervised technique
and as such does not include label information of the data. Kirby and Sirovich[15] were among
the first to apply principal component analysis (PCA) to face images, and showed that PCA is an
5. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 76
optimal compression scheme that minimizes the mean squared error between the original images
and their reconstructions for any given level of compression
PCA, mathematically defined as an orthogonal linear transformation [23] that transforms the data
to a new coordinate system such that the greatest variance by any projection of the data comes to
lie on the first coordinate (called the first principal component), the second greatest variance on the
second coordinate, and so on.
Define a data matrix, X
T
, with zero empirical mean, where each of the n rows represents a
different repetition of the experiment, and each of the m columns gives a particular kind of datum.
The singular value decomposition of X is X = WΣV
T
, where m × m matrix, W is the matrix of
eigenvectors of XX
T
, matrix Σ is an m × n rectangular diagonal matrix with nonnegative real
numbers on the diagonal, and the n × n matrix V is the matrix of eigenvectors of X
T
X .
The PCA transformation that preserves dimensionality is then given by eq (3).
WXY TT
= WWV TT
∑= ∑= T
V (3).
V is not uniquely defined in the usual case when m < n − 1, but Y will usually still be uniquely
defined. Since W is an orthogonal matrix, each row of Y
T
is simply a rotation of the corresponding
row of X
T
. The first column of Y
T
is made up of the "scores" of the cases with respect to the
"principal" component; the next column has the scores with respect to the "second principal"
component, and so on. For reduced-dimensionality representation, project X down into the
reduced space defined by only the first L singular vectors, WL,
T
L
T
L VXWY ∑==
Where with mLI × the mL× rectangular identity matrix. The matrix W of singular vectors of X
is equivalently the matrix W of eigenvectors of the matrix of observed covariances
T
XXC = ,
∑∑= TTT
WWXX. (4).
Given a set of points in Euclidean space, the first principal component corresponds to a line that
passes through the multidimensional mean and minimizes the sum of squares of the distances of
the points from the line. The second principal component corresponds to the same concept after
all correlation with the first principal component has been subtracted from the points. The singular
values in Σ are the square roots of the eigenvalues of the matrix XX
T
. Each eigenvalue is
proportional to the portion of the "variance" that is correlated with each eigenvector. The sum of
all the eigenvalues is equal to the sum of the squared distances of the points from their
multidimensional mean. PCA essentially rotates the set of points around their mean in order to
align with the principal components. This moves as much of the variance as possible (using an
orthogonal transformation) into the first few dimensions. The values in the remaining dimensions,
therefore, tend to be small and may be dropped with minimal loss of information. PCA is often
used in this manner for dimensionality reduction. PCA has the distinction of being the optimal
orthogonal transformation for keeping the subspace that has largest "variance". One
characteristic of both PCA and LDA is that they produce spatially global feature vectors. In other
words, the basis vectors produced by PCA and LDA are non-zero for almost all dimensions,
implying that a change to a single input pixel will alter every dimension of its subspace projection.
2.3. Local Binary Pattern for Iris pattern
Local Binary Pattern (LBP) is an efficient method used for feature extraction and texture
classification it was first introduced by Ojala et al in 1996 [19] , this was the first article to
describe LBP. The LBP operator was introduced as a complementary measure for local image
6. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 77
contrast, and it was developed as a grayscale invariant pattern measure adding complementary
information to the amount of texture in images. LBP is ideally suited for applications requiring fast
feature extraction and texture classificationLocal Binary Pattern (LBP) is a very efficient texture
operator which labels the pixels of an image by thresholding the neighborhood of each pixel and
considers the result as a binary number. Due to its discriminative power and computational
simplicity, LBP texture operator has become a popular approach in various applications. . Local
binary patterns are adopted for representing the textural characteristics of local sub-regions.It can
be seen as a unifying approach to the traditionally divergent statistical and structural models of
texture analysis. Each iris images can be considered as a composition of micro-patterns which
can be effectively detected by the LBP operator .The LBP operator [18] forms labels for the image
pixels by thresholding the 3 x 3 neighborhood of each pixel with the center value and considering
the result as a binary number. The histogram of these 2
8
= 256 different labels can then be used
as a texture descriptor. The features of the iris pattern is extracted using the above procedure
and is explained in Fig.2
FIGURE 2: Calculating the Original LBP Code and A Contrast Measure.
3. Experiment and Results
In this section the performance of the proposed multibiometric recognition is tested on a real time
database consisting of 50 pair of identical twins from whom the face, fingerprint and iris images
of the persons are collected. The images are acquired in a resolution of 200x200 sizes. We have
implemented our multibiometric system in MATLAB 7.10 on a Pentium-IV Windows XP
workstation. To build our virtual multimodal database, we have chosen 100 images. Face images
are randomly sampled as training samples, and the remaining are left as test samples. The
technique is also applied for fingerprint and iris databases to collect training samples. Then, each
sample of the face database is randomly combined with one sample of the fingerprint database
and one sample of the iris database.
The performance of a biometric system can be shown as a Receiver Operating Characteristic
(ROC) curve that plots the Genuine Accept Rate against the False Accept Rate (FAR) at different
thresholds on the matching score. Fig. 5 shows the performance of the hybrid approach
presented here.
We compare this performance with other approaches that does not utilize texture information for
representing the fingerprint. As can be seen in the graph, the proposed hybrid approach
outperforms over a wide range of FAR values.
The results obtained using various multibiometric systems were analyzed and the area under the
ROC curve for each method using Real Time database are shown in Table 1., and it shows the
area under the ROC curve (Az), Standard Deviation (S.D) and 95% Confidence Interval (CI) for
each classifier. Results show that high performance was obtained by the proposed scheme when
compared to other multibiometric systems.
7. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 78
TABLE 1: Classification Results.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False positive rate (1-Specificity)
Truepositiverate(Sensitivity)
ROC curve
Unimodal PCA
Multimodal PCA
Proposed
Figure 3. Receiver Operating Characteristics Curve.
4. CONCLUSION
This paper has presented a multimodal approach and comparison of three approaches in
implementing a biometric system. Using a multimodal database we have investigated the relative
merits of adopting a single classifier approach, an approach which uses a multimodal classifier
configuration operating on a single modality and, finally, a multimodal biometric solution which
combines different biometric samples in providing an identification decision. Our study has
provided quantitative data to demonstrate the relative performance levels, in terms of ROC curve,
attainable in each case, and we have shown how multimodal biometric solutions, while offering
other additional advantages where appropriate, provide only modest improvements over an
approach based on a multimodal classifier approach and a single modality, bringing some
potentially significant benefits in terms of usability.
5. ACKNOWLEDGMENT
The author would like to express his sincere gratitude to his research guide Perumal Sankar and
his co author Nisha George for all their helpful guidance and advice.
6. REFERENCES
[1] D. Costello, Families: The Perfect Deception: Identical Twins", Wall Street Journal, February
12, 1999.
Single mode
PCA
Multimodal
PCA
Proposed
Az 0.94470 0.96036 0.96147
S.D 0.01710 0.01413 0.01370
95% CI 0.91119 0.93268 0.93419
8. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 79
[2] J. Martin, H.-C. Kuang, T. J. Mathews, D. L. Hoyert Hoyert,D. M. Strobino, B. Guyer, and S.
R. Sutton, “Annual summary of vital statistics: 2006,” in Pediatrics,pp. 788–801, 2008.
[3] Anil K. Jain, Salil Prabhakar and Sharath Pankanti” Twin Test: On Discriminability of
Fingerprints”.
[4] Sargur N. Srihari,Harish Srinivasan,andGang Fang” Discriminability of Fingerprints of Twin’’,
Journal of Forensic Identification, March 19, 2007,pp.109-121.
[5] K. Nandakumar, A. Ross and A. K. Jain, Handbook of Multibiometrics. New York: Springer-
Verlag, 2006.
[6] Z. Sun, A. A. Paulino, J. Feng, Z. Chai, T. Tan, and A. K. Jain, “A study of multibiometric
traits of identical twins,” in Biometric Technology for Human Identification VII, B. V. K. V.
Kumar, S. Prabhakar, and A. A.Ross, Eds., vol. Proc. SPIE 7667, 2010.
[7] P. Jonathon Phillips, Patrick j. Flynn, Kevin w. Bowyer, Richa w. Vorder Bruegge Patrick j.
Grother, George w. Quinn and Matthew Pruitt,” Distinguishing identical twins by face
recognition”, automatic face & gesture recognition and workshops, IEEE Digital Explore21-25
March 2011,pp. 185 - 192
[8] M. Turk, A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience 3 (1991)
71–86.
[9] A. Kong, D. Zhang, and G. Lu, “A study of identical twins palmprints for personal
authentication.”
[10]K. Nandakumar, A. Ross and A. K. Jain, Handbook of Multibiometrics. New York: Springer-
Verlag, 2006.
[11]Kocaman, B.,Kirci, M, Gunes, E.O,Cakir, Yand Ozbudak, O.”On Ear biometrics”
,EUROCON,IEEE,2009,pp.327-332.
[12]K. Chang, K. W. Bowyer, S. Sarkar, and B. Victor, “Comparison and combination of ear and
face images in appearance-based biometrics,” IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 25 no. 9, pp. 1160- 1165, Sept. 2003.
[13]T. Heseltine, N. Pears, J. Austin, and Z. Chen, “Face recognition: A comparison of
appearance-based approaches,” in Proc. 7th Digit. Image Comput.: Tech. Appl., C. Sun, H.
Talbot, S. Ourselin, and T. Adriaansen, (Eds.), Sydney, Australia, 2003, pp. 59–68.
[14]K. Jain, S. Prabhakar, and S. Pankanti, “On the similarity of identical twin fingerprints,” 2002.
[15]M. Kirby and L. Sirovich, "Application of the Karhunen-Loeve Procedure for the
Characterization of Human Faces," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 12, pp. 103-107,1990.
[16] Nisha Srinivas, Gaurav Aggarwal, Patrick J Flynn,and Richard W. Vorder Bruegge,” Facial
Marks as Biometric Signatures to Distinguish between Identical Twins”,pp.113-120
[17]E. W. Kashiko Kodate, Rieko Inaba and T. Kamiya,“Facial recognition by a compact parallel
optical correlator,” Measurement Science and Technology, vol. 13, Nov 2002.
[18]B.A. Draper, K. Baek, M.S. Bartlett and J.R. Beveridge,“Recognizing faces with PCA and
ICA”, Computer Vision andImage Understanding, 91 (1-2), pp. 115-137, 2003.
[19] X. Jing, Y. Yao, D. Zhang, M. Li, Face and palmprint pixel level fusionand Kernel DCV-RBF
classifier for small sample biometrics recognition,Pattern Recognition 40 (11), 2007, pp.
3209–3224.
9. Dinakardas C.N, S.Perumal Sankar & Nisha George
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 80
[20]S. Ribaric, I. Fratric, A biometric identification system based on eigenpalm and eigenfinger
features, IEEE Trans. Pattern Anal. Mach.Intell. 27 (11), 2005, pp. 1698–1709.
[21]S. C. Dass, K. Nandakumar & A. K. Jain, A principal approach to scorelevel fusion in
Multimodal Biometrics System, Proceedings of ABVPA, 2005.
[22]Sheetal Chaudhary, and Rajender Nath, A Multimodal Biometric Recognition System Based
on Fusion of Palmprint, Fingerprint and Face,Proceedings of ICART in Communication and
Computing,2009.
[23]P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces:
Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell.,
Vol. 19, No. 7, pp. 711–720, Jul. 1997.