Recognizing Faces helps to name the various subjects present in the image. This work focuses
on labeling faces on an image which includes faces of humans being of various age group
(heterogeneous set ). Principal component analysis concentrates on finds the mean of the data
set and subtracts the mean value from the data set with an intention to normalize that data.
Normalization with respect to image is the removal of common features from the data set. This
work brings in the novel idea of deploying the median another measure of central tendency for
normalization rather than mean. The above work was implemented using matlab. Results show
that Median is the best measure for normalization for a heterogeneous data set which gives
raise to outliers.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
MIP AND UNSUPERVISED CLUSTERING FOR THE DETECTION OF BRAIN TUMOUR CELLSAM Publications
Image processing is widely used in biomedical applications. Image processing can be used to analyze
different MRI brain images in order to get the abnormality in the image .The objective is to extract meaningful
information from the imaged signals. Image segmentation is a process of partitioning an image in to different parts.
The division in to parts is often based on the characteristics of the pixels in the image. In our paper the segmentation
of the tumour tissues is carried out using k-means and fuzzy c-means clustering.Tumour can be found and faster
detection is achieved with only few seconds for execution. The input image of the brain is taken from the available
database and the presence of tumourin input image can be detected.
AnAccurate and Dynamic Predictive Mathematical Model for Classification and P...inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
MIP AND UNSUPERVISED CLUSTERING FOR THE DETECTION OF BRAIN TUMOUR CELLSAM Publications
Image processing is widely used in biomedical applications. Image processing can be used to analyze
different MRI brain images in order to get the abnormality in the image .The objective is to extract meaningful
information from the imaged signals. Image segmentation is a process of partitioning an image in to different parts.
The division in to parts is often based on the characteristics of the pixels in the image. In our paper the segmentation
of the tumour tissues is carried out using k-means and fuzzy c-means clustering.Tumour can be found and faster
detection is achieved with only few seconds for execution. The input image of the brain is taken from the available
database and the presence of tumourin input image can be detected.
AnAccurate and Dynamic Predictive Mathematical Model for Classification and P...inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Kashif Mehmood
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays
the vital role to address challenges such as optimal generation, economic scheduling, dispatching and
contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN) technique to perform STFL but long training time and convergence issues caused by bias,
variance and less generalization ability, unable this algorithm to accurately predict future loads. This
issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint
partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and
increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process
of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of
this method by taking mean improves the overall performance. This method of combining several
predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging
method by further increasing the generalization ability and STLF accuracy.
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.
An image is a medium for conveying information. The information contained therein may be a particular event, experience or moment. Not infrequently many images that have similarities. However, this level of similarity is not easily detected by the human eye. Eigenface is one technique to calculate the resemblance of an object. This technique calculates based on the intensity of the colors that exist in the two images compared. The stages used are normalization, eigenface, training, and testing. Eigenface is used to calculate pixel proximity between images. This calculation yields the feature value used for comparison. The smallest value of the feature value is an image very close to the original image. Application of this method is very helpful for analysts to predict the likeness of digital images. Also, it can be used in the field of steganography, digital forensic, face recognition and so forth.
A Study on Youth Violence and Aggression using DEMATEL with FCM Methodsijdmtaiir
The DEMATEL method is then a good technique for
making decisions. In this paper we analyzed the risk factors of
youth violence and what makes them more aggressive. Since
there are more risk factors of youth violence, to relate each
other more complex to construct FCM and analyze them.
Moreover the data is an unsupervised one obtained from
survey as well as interviews. Hence fuzzy alone has the
capacity to analyses these concepts.
Content adaptive single image interpolation based Super Resolution of compres...IJECEIAES
Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling. This paper focuses on upscaling of compressed images with interpolation based Single Image Super Resolution technique. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super Resolution methods. The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on a single image and uses the content of the original image only; therefore, the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.
Face Recognition using Discrete Wavelet Transform and Principle Component Analysis features of MATLAB.
for processing video go to: https://www.youtube.com/watch?v=X67b0NULO98
A Bayesian approach to estimate probabilities in classification treesNTNU
Classification or decision trees are one of the most effective methods for supervised clas- sification. In this work, we present a Bayesian approach to induce classification trees based on a Bayesian score splitting criterion and a new Bayesian method to estimate the probability of class membership based on Bayesian model averaging over the rules of the previously induced tree. In an experimental evaluation, we show as our approach reaches the performance of Quinlan’s C4.5, one of the most known decision tree inducers, in terms of predictive accuracy and clearly outperforms it in terms of better probability class estimates.
Image Fusion and Image Quality Assessment of Fused ImagesCSCJournals
Accurate diagnosis of tumor extent is important in radiotherapy. This paper presents the use of image fusion of PET and MRI image. Multi-sensor image fusion is the process of combining information from two or more images into a single image. The resulting image contains more information as compared to individual images. PET delivers high-resolution molecular imaging with a resolution down to 2.5 mm full width at half maximum (FWHM), which allows us to observe the brain\'s molecular changes using the specific reporter genes and probes. On the other hand, the 7.0 T-MRI, with sub-millimeter resolution images of the cortical areas down to 250 m, allows us to visualize the fine details of the brainstem areas as well as the many cortical and sub-cortical areas. The PET-MRI fusion imaging system provides complete information on neurological diseases as well as cognitive neurosciences. The paper presents PCA based image fusion and also focuses on image fusion algorithm based on wavelet transform to improve resolution of the images in which two images to be fused are firstly decomposed into sub-images with different frequency and then the information fusion is performed and finally these sub-images are reconstructed into result image with plentiful information. . We also propose image fusion in Radon space. This paper presents assessment of image fusion by measuring the quantity of enhanced information in fused images. We use entropy, mean, standard deviation and Fusion Mutual Information, cross correlation , Mutual Information Root Mean Square Error, Universal Image Quality Index and Relative shift in mean to compare fused image quality. Comparative evaluation of fused images is a critical step to evaluate the relative performance of different image fusion algorithms. In this paper, we also propose image quality metric based on the human vision system (HVS).
A Prediction Model for Taiwan Tourism Industry Stock Indexijcsit
Investors and scholars pay continuous attention to the stock market, as each day, many investors attempt to
use different methods to predict stock price trends. However, as stock price is affected by economy, politics,
domestic and foreign situations, emergency, human factor, and other unknown factors, it is difficult to
establish an accurate prediction model. This study used a back-propagation neural network (BPN) as the
research approach, and input 29 variables, such as international exchange rate, indices of international
stock markets, Taiwan stock market analysis indicators, and overall economic indicators, to predict
Taiwan’s monthly tourism industry stock index. The empirical findings show that the BPN prediction model
has better predictive accuracy, Absolute Relative Error is 0.090058, and correlation coefficient is
0.944263. The model has low error and high correlation, and can serve as reference for investors and
relevant industries
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is
appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access
control, and electronic customer relationship management. Current deep learning-based methods have
displayed encouraging performance in age estimation field. Males and Females have a variable type of
appearance aging pattern; this results in age differently. This fact leads to assuming that using gender
information may improve the age estimator performance. We have proposed a novel model based on
Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then
Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task.
Bayesian Optimization reduces the classification error on the validation set for the pre-trained model.
Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The
experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian
Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute
Error (MAE) of 1.2 and 2.67 respectively.
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.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Kashif Mehmood
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays
the vital role to address challenges such as optimal generation, economic scheduling, dispatching and
contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN) technique to perform STFL but long training time and convergence issues caused by bias,
variance and less generalization ability, unable this algorithm to accurately predict future loads. This
issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint
partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and
increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process
of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of
this method by taking mean improves the overall performance. This method of combining several
predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging
method by further increasing the generalization ability and STLF accuracy.
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.
An image is a medium for conveying information. The information contained therein may be a particular event, experience or moment. Not infrequently many images that have similarities. However, this level of similarity is not easily detected by the human eye. Eigenface is one technique to calculate the resemblance of an object. This technique calculates based on the intensity of the colors that exist in the two images compared. The stages used are normalization, eigenface, training, and testing. Eigenface is used to calculate pixel proximity between images. This calculation yields the feature value used for comparison. The smallest value of the feature value is an image very close to the original image. Application of this method is very helpful for analysts to predict the likeness of digital images. Also, it can be used in the field of steganography, digital forensic, face recognition and so forth.
A Study on Youth Violence and Aggression using DEMATEL with FCM Methodsijdmtaiir
The DEMATEL method is then a good technique for
making decisions. In this paper we analyzed the risk factors of
youth violence and what makes them more aggressive. Since
there are more risk factors of youth violence, to relate each
other more complex to construct FCM and analyze them.
Moreover the data is an unsupervised one obtained from
survey as well as interviews. Hence fuzzy alone has the
capacity to analyses these concepts.
Content adaptive single image interpolation based Super Resolution of compres...IJECEIAES
Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling. This paper focuses on upscaling of compressed images with interpolation based Single Image Super Resolution technique. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super Resolution methods. The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on a single image and uses the content of the original image only; therefore, the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.
Face Recognition using Discrete Wavelet Transform and Principle Component Analysis features of MATLAB.
for processing video go to: https://www.youtube.com/watch?v=X67b0NULO98
A Bayesian approach to estimate probabilities in classification treesNTNU
Classification or decision trees are one of the most effective methods for supervised clas- sification. In this work, we present a Bayesian approach to induce classification trees based on a Bayesian score splitting criterion and a new Bayesian method to estimate the probability of class membership based on Bayesian model averaging over the rules of the previously induced tree. In an experimental evaluation, we show as our approach reaches the performance of Quinlan’s C4.5, one of the most known decision tree inducers, in terms of predictive accuracy and clearly outperforms it in terms of better probability class estimates.
Image Fusion and Image Quality Assessment of Fused ImagesCSCJournals
Accurate diagnosis of tumor extent is important in radiotherapy. This paper presents the use of image fusion of PET and MRI image. Multi-sensor image fusion is the process of combining information from two or more images into a single image. The resulting image contains more information as compared to individual images. PET delivers high-resolution molecular imaging with a resolution down to 2.5 mm full width at half maximum (FWHM), which allows us to observe the brain\'s molecular changes using the specific reporter genes and probes. On the other hand, the 7.0 T-MRI, with sub-millimeter resolution images of the cortical areas down to 250 m, allows us to visualize the fine details of the brainstem areas as well as the many cortical and sub-cortical areas. The PET-MRI fusion imaging system provides complete information on neurological diseases as well as cognitive neurosciences. The paper presents PCA based image fusion and also focuses on image fusion algorithm based on wavelet transform to improve resolution of the images in which two images to be fused are firstly decomposed into sub-images with different frequency and then the information fusion is performed and finally these sub-images are reconstructed into result image with plentiful information. . We also propose image fusion in Radon space. This paper presents assessment of image fusion by measuring the quantity of enhanced information in fused images. We use entropy, mean, standard deviation and Fusion Mutual Information, cross correlation , Mutual Information Root Mean Square Error, Universal Image Quality Index and Relative shift in mean to compare fused image quality. Comparative evaluation of fused images is a critical step to evaluate the relative performance of different image fusion algorithms. In this paper, we also propose image quality metric based on the human vision system (HVS).
A Prediction Model for Taiwan Tourism Industry Stock Indexijcsit
Investors and scholars pay continuous attention to the stock market, as each day, many investors attempt to
use different methods to predict stock price trends. However, as stock price is affected by economy, politics,
domestic and foreign situations, emergency, human factor, and other unknown factors, it is difficult to
establish an accurate prediction model. This study used a back-propagation neural network (BPN) as the
research approach, and input 29 variables, such as international exchange rate, indices of international
stock markets, Taiwan stock market analysis indicators, and overall economic indicators, to predict
Taiwan’s monthly tourism industry stock index. The empirical findings show that the BPN prediction model
has better predictive accuracy, Absolute Relative Error is 0.090058, and correlation coefficient is
0.944263. The model has low error and high correlation, and can serve as reference for investors and
relevant industries
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is
appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access
control, and electronic customer relationship management. Current deep learning-based methods have
displayed encouraging performance in age estimation field. Males and Females have a variable type of
appearance aging pattern; this results in age differently. This fact leads to assuming that using gender
information may improve the age estimator performance. We have proposed a novel model based on
Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then
Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task.
Bayesian Optimization reduces the classification error on the validation set for the pre-trained model.
Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The
experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian
Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute
Error (MAE) of 1.2 and 2.67 respectively.
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.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
FACE RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS WITH MEDIAN FOR NORMALIZA...cscpconf
Recognizing Faces helps to name the various subjects present in the image. This work focuses on labeling faces on an image which includes faces of humans being of various age group
(heterogeneous set ). Principal component analysis concentrates on finds the mean of the data set and subtracts the mean value from the data set with an intention to normalize that data. Normalization with respect to image is the removal of common features from the data set. This work brings in the novel idea of deploying the median another measure of central tendency for normalization rather than mean. The above work was implemented using matlab. Results show that Median is the best measure for normalization for a heterogeneous data set which gives raise to outliers.
APPLICATION OF IMAGE FUSION FOR ENHANCING THE QUALITY OF AN IMAGEcscpconf
Advances in technology have brought about extensive research in the field of image fusion.
Image fusion is one of the most researched challenges of Face Recognition. Face Recognition
(FR) is the process by which the brain and mind understand, interpret and identify or verify
human faces.. Image fusion is the combination of two or more source images which vary in
resolution, instrument modality, or image capture technique into a single composite
representation. Thus, the source images are complementary in many ways, with no one input
image being an adequate data representation of the scene. Therefore, the goal of an image
fusion algorithm is to integrate the redundant and complementary information obtained from
the source images in order to form a new image which provides a better description of the scene
for human or machine perception. In this paper we have proposed a novel approach of pixel
level image fusion using PCA that will remove the image blurredness in two images and
reconstruct a new de-blurred fused image. The proposed approach is based on the calculation
of Eigen faces with Principal Component Analysis (PCA). Principal Component Analysis (PCA)
has been most widely used method for dimensionality reduction and feature extraction
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
Abstract Face recognition is a form of computer vision that uses faces to identify a person or verify a person’s claimed identity. In this paper, a neural based algorithm is presented, to detect frontal views of faces. The dimensionality of input face image is reduced by the Principal component analysis and the Classification is by the neural back propagation network. This method is robust for a dataset of 300 face images and has better performance in terms of 80 – 90 % recognition rate.
Face Recognition System Using Local Ternary Pattern and Signed Number Multipl...inventionjournals
This paper presents a novel approach to face recognition. The task of face recognition is to verify a claimed identity by comparing a claimed image of the individual with other images belonging to the same individual/other individual in a database. The proposed method utilizes Local Ternary Pattern and signed bit multiplication to extract local features of a face. The image is divided into small non-overlapping windows. Processing is carried out on these windows to extract features. Test image’s features are compared with all the training images using Euclidean's distance. The image with lowest Euclidean distance is recognized as the true face image. If the distance between test and all training images is more than threshold then test image is considered as unrecognised image or match not found .The face recognition rate of proposed system is calculated by varying the number of images per person in training database
K-MEDOIDS CLUSTERING USING PARTITIONING AROUND MEDOIDS FOR PERFORMING FACE R...ijscmc
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality
Medoid based model for face recognition using eigen and fisher facesijscmcj
Biometric technologies have gained a remarkable impetus in high security applications. Various biometric modalities are widely being used these days. The need for unobtrusive biometric recognition can be fulfilled through Face recognition which is the most natural and non intrusive authentication system. However the vulnerability to changes owing to variations in face due to various factors like pose,
illumination, ageing, emotions, expressions etc make it necessary to have robust face recognition systems.
Various statistical models have been developed so far with varying degree of accuracy and efficiency. This
paper discusses a new approach to utilize Eigen face and Fisher face methodology by using medoid instead
of mean as a statistic in calculating the Eigen faces and Fisher faces. The method not only requires lesser training but also demonstrates better time efficiency and performance compared to the conventional method of using mean
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...IJCSEIT Journal
A face recognition system using different local features with different distance measures is proposed in this
paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values,
Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local
features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector
and diagonal vectors are computed for these matrices. Global feature vector is generated for face
recognition. Experiments are performed on benchmark face YALE database. Results indicate that the
proposed method gives better recognition performance in terms of average recognized rate and retrieval
time compared to the existing methods.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Neural Network based Supervised Self Organizing Maps for Face Recognition ijsc
The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed method to a variety of datasets and show the results.
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITIONijsc
The word biometrics refers to the use of physiological or biological characteristics of human to recognize
and verify the identity of an individual. Face is one of the human biometrics for passive identification with
uniqueness and stability. In this manuscript we present a new face based biometric system based on neural
networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the
proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed
method to a variety of datasets and show the results.
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
Recognition of different internal emotions of human face under various critical
conditions is a difficult task. Facial expression recognition with different age variations is
considered in this study. This paper emphasizes on recognition of facial expression like
happiness mood of nine persons using subspace methods. This paper mainly focuses on new
robust subspace method which is based on Proposed Euclidean Distance Score Level Fusion
(PEDSLF) using PCA, ICA, SVD methods. All these methods and new robust method is
tested with FGNET database. An automatic recognition of facial expressions is being carried
out. Comparative analysis results surpluses PEDSLF method is more accurate for happiness
emotional facial expression recognition.
LITERATURE SURVEY ON SPARSE REPRESENTATION FOR NEURAL NETWORK BASED FACE DETE...csijjournal
Face detection and recognition is a challenging problem in the field of image processing. In this paper, we reviewed some of the recent research works on face recognition. Issues with the previous face recognition
techniques are , time required is more for face recognition , recognition rate and database required to store the data . To overcome these problems sparse representation based classifier technique can be used.
An efficient feature extraction method with pseudo zernike moment for facial ...ijcsity
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. Face recognition system is critical when individuals have very similar biometric signature such as
identical twins. In this paper, new efficient facial-based identical twins recognition is proposed according
to the geometric moment. The utilized geometric moment is Pseudo-Zernike Moment (PZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area inside an image is detected
using Ada Boost approach. The proposed method is evaluated on two datasets, Twins Days Festival and
Iranian Twin Society which contain scaled, which contain the shifted and rotated facial images of identical
twins in different illuminations. The results prove the ability of proposed method to recognize a pair of
identical twins. Also, results show that the proposed method is robust to rotation, scaling and changing
illumination.
CDS is the criminal face identification by capsule neural network.
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middle aged man or human from different parts(heterogeneous data set) of the world whose face
carry few common details compared to human from the same nation(homogeneous data set).
Figure 1.1 shows few subjects which are considered for face recognition.
Figure 1.1 Heterogeneous data set
1.2. Principal Component Analysis(PCA):
The Eigenfaces method is a very proven method which was introduced by Turk and Pentland
1991. Eigen faces method is based on Principal Component Analysis technique for data
reduction and feature extraction
extraction[8]. Principal component analysis (PCA) is probably the most
popular multivariate statistical technique and it is used by almost all scientific disciplines
disciplines[11]. Its
goal is to extract the important information from the image and to express this information as a
information
set of new orthogonal variables called principal components . It can be used for feature
components[8].
extraction, compression, classification, and dimension reduction et cetera. The aim of this paper
fication,
is to introduce a modification is the Principal component analysis technique which is vital in the
duce
Principal
normalization of the input data set and leads to a construction of well defined eigenfaces which is
subsequently used for Face recognition in a heterogeneous data set[9].
The remaining paper is organized as follows: Related work is discussed in Section 2 and
Proposed method in Section 3. Experimental results are shown in Section 4 and Conclusion is
C
given in Section 5.
2. RELATED WORK
Principal Component analysis PCA has been widely investigated and has become one of the
most successful approaches in face recognition[5]. Principal component analysis (PCA), also
known as Karhunen-Loeve expansion or Hotelling Transformation, is a classical feature
Loeve
extraction and data representation technique widely used in the areas of pattern recognition and
on
computer vision[6]. Principal Component Analysis (PCA) is one of the most successful
techniques that have been used in image recognition and compression.
The paper titled Face recognition using PCA, eigenface and ANN (Artificial Neural Networks)
has shown 97.018% of accuracy on Olivetti and Oracle Research Laboratory (ORL) face
database [1]. In A Discriminative Model for Age Invariant Face Recognition by Zhifeng Li et al,
by
3. Computer Science & Information Technology (CS & IT)
149
PCA is performed on the training set, after extracting the SIFT (Scale in variant feature
transformation ) or MLBP (Multi - Scale local binary patterns ) and all the eigenvectors with nonzero eigenvalues are used as the candidates for constructing random PCA subspaces [2]. In the
paper Face recognition using Pca, Lda and Ica approaches on colored images, experimental
results have shown that PCA out performs LDA( Linear Discriminant Analysis and
ICA(Independent Component Analysis)[3]. In Combined face and gait recognition using alpha
matte preprocessing principal component analysis (PCA) followed by multiple discriminant
analysis (MDA) to reduce the size of the feature vector. A combination of PCA and MDA,
results in the best recognition performance[4]. Using principal-component analysis (PCA), many
face recognition techniques have been developed: namely eigenfaces [Turk and Pentland 1991],
which use a nearest- neighbor classifier and feature-line-based methods, which replace the pointto-point distance with the distance between a point and the feature line linking two stored sample
points [Li and Lu 1999] are developed and deployed.
3. PROPOSED METHOD
Input to the Face recognition system is set of faces. The data set considered comprises of faces
belonging to various regions in the globe and subjects of young age, babies and old aged people.
This heterogeneous data set is taken with an assumption that all the images considered are of the
same size 140X175. The figure 1.1 shows the sample images considered for recognition.
The original PCA algorithm subtract the mean value from each input data. Mean is a single
value which gives the central tendency of measure. Measures of central tendency are sometimes
called measures of central location. The mean, median and mode are all valid measures of central
tendency, but under different conditions, some measures of central tendency become more
appropriate to use than others. The mean is only representative if the distribution of the data is
symmetric, otherwise it may be heavily influenced by outlying (outliers) measurements. In such
conditions median is always best representative of the centre of the data. Mathematically the
median is preferred over the mean (or mode) when our data is skewed (i.e., the frequency
distribution for our data is skewed)[7] . The measure asymmetric of a distribution is called
skewness.
3.1 Steps in Principal Component Analysis using Median for Normalization:
1. The First step is to construct a training data set with M images of same size.
2. Convert the RGB image to a Gray Scale image.
3. Convert each face 2-Dimensional image data into a face vector of 1 Dimension by
concatenating each row to the 1st row in a chronological order.
The Figure 3.1 shows the intensity value of the pixels for the images considered where each row
represents face vector of one person in the data set.
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Computer Science & Information Technology (CS & IT)
Figure 3.1- Face vectors representing the pixel intensity values.
4. Steps for Normalization
4.1 Construct a Matrix T with each row containing the face information of one person in the
data set.
4.2 Calculate the Median vector Md, for T column wise.
The following Screen Shot shows that the values are not symmetric and calculation of the
skewness. Skewness being the measure for the degree of asymmetry of a distribution is
calculated to determine whether mean or median is to be used The Screen shot reveal that
used.
Skewness value corresponding to the column considered is +ve (1.5132). Similarly the skewness
of each column is considered and show either a negative or positive value hence median is
considered over the mean.
5. Subtract the Median vector Md for the corresponding column with each column value. The
result obtained is normalized matrix N which is represented by Ø. Normalization in image is to
remove the common features from each face and each face is left with unique features.
5. Computer Science & Information Technology (CS & IT)
151
i.e., Ni = Ti- Md
6. Next proceed with the Calculation of the EigenVector and EigenValues, EigenValues are
obtained from the Covariance matrix C the normalized matrix Ni. The Covariance matrix C is
calculated using the formula
C= (Ni) (Ni)T
7. Now proceed with the calculation of the eigen values and the eigenvectors using [V,D] =
eig(C).
8. Select K < M eigen vectors corresponding to the greatest eigen values in the matrix D.This K
eigen vectors are used to reconstruct all the M faces in the Training set.
9. The eigenvectors are also called as eigen faces must be of the original dimensionality of the
face vectors. So map the eigen vector in lower dimensional space to the corresponding
eigenvector in the higher dimensional space.Suppose Ui, Vi are the eigen vector in higher
dimensional space and lower dimensional space then
Ui = A . Vi
10. Steps for reconstruction of the faces in the heterogeneous data set.
Final Data = Ui x Ni
Ni = (Final Data)T X Ui
Original data set = (Final Data)T X Ui+ Md
11. Recognition
For recognition express each image in the training set as a linear combination of the eigenfaces
plus the median image. When considering the linear combination of the eigenfaces calculate the
weight associated with each eigenface. The weight determine the proportion of contribution of
the specific eigenface towards the reconstruction of the original face. Once the weight vector for
all the images in the data set is determine, to recognize the unknown face, we have repeat the
step1 to step5 of the above specified algorithm. Then try the represent the unknown face as a
combination of the already constructed eigenfaces and determine the weight vector. If the weight
vector for the unknown face is one among the weight vector of the data set already considered
then we declare the face as a known face other vice versa.
4. EXPERIMENTAL RESULTS
Table 1 shows the heterogeneous images considered for appling pca using median for
normalization, The median image obtained and the reconstructed image are shown
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Computer Science & Information Technology (CS & IT)
Table 1. Input images, median image and reconstructed image
.
Input Images
Median Image
Reconstructed
image from the
eigen faces
5. CONCLUSION
From the experimental results it is clear that face reconstruction with the eigenface after
eigenfaces
normalizing the data set using Median as a Central Measure is efficient on a Heterogeneous data
set. Recognition follows effectively as subsequent step after the reconstruction. Future work will
be to concentrate on methodology for determining the weight vector which is impact apply PCA
for Face recognition.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
Agrawal, H. ; Jain, N. ; Kumar. M. (2010) "Face Recognition using Principle Component Analysis,
Eigenface and Neural Network", International Conference on Signal Acquisition an Processing
and
published in IEEE Computer Society, pp 310 -314.
Zhifeng Li, Member, Unsang Park, Member, and Anil K. Jain, (2011) "A Discriminative Model for
Age Invariant Face Recognition", IEEE Transactions on information forensics and security, V 6,
Vol.
No. 3, September pp 1028 -1037.
1037.
Önsen TOYGAR1, Adnan ACAN2, (2003) " Face recognition using PCA, LDA and ICA approaches
on colored images" Journal of electrical & electronics engineering Vol. No. :: 3 : 1 pp (735
(735-743).
Hofmann, M.Schmidt, S.M., Rajagopalan, A.N. Rigoll. G (2012), "Combined face and gait
,
recognition using alpha matte preprocessing" , Proceedings 5th IAPR International Conference on
Biometrics (ICB).
A. Pentland, (2000), “Looking at People: Sensing for Ubiquitous and Wearable Computing,”, IEEE
Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 107
107-119.
Jian Yang, David Zhang, Senior Member , Alejandro F. Frangi, and Jing yu Yang, (2004), "TwoJing-yu
"Two
Dimensional PCA: A New Approach to Appearance
Appearance-Based Face Representation and Recognition" ,
ace
Transactions on pattern analysis and machine intelligence, Vol. 26, No. 1, pp 131 –137.
137.
Handbook of Face Recognition edited by Stan Z. Li, Anil K. Jain.
Matthew Turk and Alex Pentland, (1991) " Eigen faces method", Journal of Cognitive NeuroScience
method",
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Patrik Kamencay, Martina Zachariasova, Robert Hudec, Roman Jarina, Miroslav Benco, Jan Hlubik,
(2013) " A Novel Approach to Face Recognition using Image Segmentation Based on SPCA-KNN
Method", Radio Engineering Vol 22, No1.
[10] M A Rabbani and C. Chellappan, (2007) "A Different Approach to Appearance –based Statistical
Method for Face Recognition Using Median", IJCSNS International Journal of Computer Science
and Network Security Vol 7, No. 4, pp : 262-267
[11] A tutorial on Principal Components Analysis Lindsay I Smith February 26, 2002
www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf.
AUTHORS
G. Shreedevi presents working as Assistant Professor(Sr.Grade) in the Department of
Computer Applications, B. S Abdur Rahman University. Pursuing Ph. D in Image
Processing under the able guidance of Dr. Munir Ahamed Rabbani.
Dr.M.Munir Ahamed Rabbani Professor Department of Computer Applications, B. S
Abdur Rahman . Completed his Ph. D in Area of Image Processing. Posses rich
International Experience in the Field of Teaching and Research.
Dr. Jaya Professor, Department of Computer Application, B. S. Abdur Rahman
University. Completed Ph. D in area of Artificial Intelligence.