The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is typically used as a pre-processing step to extract the face regions from human image. In past, there are several computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then skin classifier model have been applied to label the pixel into skin or non-skin regions. Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models for skin pixel detection applied on human images” would be performed which involve the process of image representation in color models, elimination of non-skin pixels in the image, and then pre-processing and cleansing of the collected data, feature selection of the human image and then building the model for classifier. In this research and implementation of skin pixels classifier models are proposed with their comparative performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human image or stack of human images. The performance is evaluated by comparing and analysing skin colour segmentation algorithms. During the course of research implementation, efforts are iterative which help in selection of optimized skin classifier based on the machine learning algorithms and their performance analysis.
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
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.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
FACE SKETCH GENERATION USING EVOLUTIONARY COMPUTING ijsc
In this paper, an evolutionary genetic algorithm is used to generate face sketch from the face description. Face sketch generation without face image is extremely important for the law enforcement agencies. The genetic algorithm is used for generating face sketch through several iterations of the lgorithm. The face image description is captured through graphical user interface just by clicking options for each face
features. Face features are used to extract face images and generate initial population for the genetic algorithm. Genetic operators such as selection, crossover and mutation are used for next generation of the population. The Genetic algorithm cycle is repeated until the user is satisfied with face sketch generated. The novelty of the paper includes face sketch generation from face image description. The result shows that
evolutionary based technique for sketch generation produces the desired face sketch.
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.
Skin Detection Based on Color Model and Low Level Features Combined with Expl...IJERA Editor
Skin detection is active research area in the field of computer vision which can be applied in the application of
face detection, eye detection, etc. These detection helps in various applications such as driver fatigue monitoring
system, surveillance system etc. In Computer vision applications, the color model and representations of the
human image in color model is one of major module to detect the skin pixels. The mainstream technology is
based on the individual pixels and selection of the pixels to detect the skin part in the whole image. In this thesis
implementation, we presents a novel technique for skin color detection incorporating with explicit region based
and parametric based approach which gives the better efficiency and performances in terms of skin detection in
human images. Color models and image quantization technique is used to extract the regions of the images and
to represent the image in a particular color model such as RGB and HSV, and then the parametric based
approach is applied by selecting the low level skin features are applied to extract the skin and non-skin pixels of
the images. In the first step, our technique uses the state-of-the-art non-parametric approach which we call the
template based technique or explicitly defined skin regions technique. Then the low level features of the human
skin are being extracted such as edge, corner detection which is also known as parametric method. The
experimental results depict the improvement in detection rate of the skin pixels by this novel approach. And in
the end we discuss the experimental results to prove the algorithmic improvements.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
A Review on Feature Extraction Techniques and General Approach for Face Recog...Editor IJCATR
In recent time, alongwith the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. Several feature extraction techniques are available such as template based, appearancebased,
geometry based, color segmentation based, etc. This paper presents an overview of various feature extraction techniques
followed in different reasearches for face recognition in the field of digital image processing and gives an approach for using these
feature extraction techniques for efficient face recognition
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
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.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
FACE SKETCH GENERATION USING EVOLUTIONARY COMPUTING ijsc
In this paper, an evolutionary genetic algorithm is used to generate face sketch from the face description. Face sketch generation without face image is extremely important for the law enforcement agencies. The genetic algorithm is used for generating face sketch through several iterations of the lgorithm. The face image description is captured through graphical user interface just by clicking options for each face
features. Face features are used to extract face images and generate initial population for the genetic algorithm. Genetic operators such as selection, crossover and mutation are used for next generation of the population. The Genetic algorithm cycle is repeated until the user is satisfied with face sketch generated. The novelty of the paper includes face sketch generation from face image description. The result shows that
evolutionary based technique for sketch generation produces the desired face sketch.
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.
Skin Detection Based on Color Model and Low Level Features Combined with Expl...IJERA Editor
Skin detection is active research area in the field of computer vision which can be applied in the application of
face detection, eye detection, etc. These detection helps in various applications such as driver fatigue monitoring
system, surveillance system etc. In Computer vision applications, the color model and representations of the
human image in color model is one of major module to detect the skin pixels. The mainstream technology is
based on the individual pixels and selection of the pixels to detect the skin part in the whole image. In this thesis
implementation, we presents a novel technique for skin color detection incorporating with explicit region based
and parametric based approach which gives the better efficiency and performances in terms of skin detection in
human images. Color models and image quantization technique is used to extract the regions of the images and
to represent the image in a particular color model such as RGB and HSV, and then the parametric based
approach is applied by selecting the low level skin features are applied to extract the skin and non-skin pixels of
the images. In the first step, our technique uses the state-of-the-art non-parametric approach which we call the
template based technique or explicitly defined skin regions technique. Then the low level features of the human
skin are being extracted such as edge, corner detection which is also known as parametric method. The
experimental results depict the improvement in detection rate of the skin pixels by this novel approach. And in
the end we discuss the experimental results to prove the algorithmic improvements.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
A Review on Feature Extraction Techniques and General Approach for Face Recog...Editor IJCATR
In recent time, alongwith the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. Several feature extraction techniques are available such as template based, appearancebased,
geometry based, color segmentation based, etc. This paper presents an overview of various feature extraction techniques
followed in different reasearches for face recognition in the field of digital image processing and gives an approach for using these
feature extraction techniques for efficient face recognition
Human Face Detection Systemin ANew AlgorithmIOSRjournaljce
Trying to detecting a face from any photo is big problem and got these days a focusing because of it importance, in face recognition system,face detection in one of the basic components. A lot of troubles are there to be solved in order to create a successful face detection algorithm. The skin of face has its properties in color domain and also a texture which may help in the algorithm for detecting faces because of its ability to find skins from photo. Here we are going to create a new algorithm for human face detection depending on skin color tone specially YCbCr color tone as an approach to slice the photo into parts. In addition, Gray level has been used to detect the area which contains a skin, after that anotherlevel used to erase the area that does not contain skin. The system which proposed applied on many photos and passed with great accuracy of detecting faces and it has a good efficient especially to separate the area that does not contain skin or face from the area which contain face and skin. It has been agreed and approved that the accuracy of the proposed system is 98% in human face detection.
50Combining Color Spaces for Human Skin Detection in Color Images using Skin ...idescitation
Skin detection remains a challenging task over
several decades in spite of many techniques evolved. It is the
elementary step of most of the computer vision applications
like face recognition, human computer interaction, etc. It
depends on the suitability of color space chosen, skin modeling
and classification of skin and non-skin pixels under varying
illumination conditions. This paper presents a symbolic
interpretation on the performance of the color spaces using
piecewise linear decision boundary classifier in color images
to find the winning color space (s). The whole task is divided
into three processes: analysis of color spaces individually;
analysis of the combination of two color spaces; and finally
making a comparative analysis among the results obtained by
the above two processes. For performing the fair evaluation,
the whole experiment is tested over commonly used databases.
Based on the success rate, false positive and false negative of
each color spaces, the winner(s) has been chosen among single
and the combination of color spaces.
A novel approach for performance parameter estimation of face recognition bas...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Hand gesture recognition system has received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. In this work Hand segmentation using color models is introduced for obtaining hand gestures or detecting user’s hand by color segmentation technique for faster, better, robust, accurate and real-time applications. There are many such color models available for human hand and human skin detection with relative advantages and disadvantages in the field of Image Processing. For the purpose of hand Segmentation mix model approach has been adopted for best results. For detection of Hand from an image. The proposed approach is found to be accurate and effective for multiple conditions
Recent developments in paper currency recognition systemeSAT Journals
Abstract
Currency denomination recognition is one the active research topics at present. And this wide interest is particularly due to the
various potential applications it has. Monetary transaction is an integral part of our day to day activities. However, blind people
particularly suffer in monetary transactions. They are not able to effectively distinguish between various denominations and are often
deceived by other people. Also, a reliable currency recognition system could be used in any sector wherever monetary transaction is
of concern. Thus, there is an ardent need to design a system that is helpful in recognition of paper currency notes correctly. Currency
denomination detection is a vast area of research and significant progress had been achieved over the years. This paper presents an
extensive survey of research on various developments in recent years in identification of currency denomination. A number of
techniques applied by various researchers are discussed briefly in order to assess the state of art.
Keywords:- Currency recognition system, Artificial Neural Network
Feature extraction is becoming popular in face recognition method. Face recognition is the interesting and growing area in real time applications. In last decades many of face recognitions methods has been developed. Feature extraction is the one of the emerging technique in the face recognition methods. In this method an attempt to show best faces recognition method. Here used different descriptors combination like LBP and SIFT, LBP and HOG for feature extraction. Using a single descriptor is difficult to address all variations so combining multiple features in common. Find LBP and SIFT features separately from the images and fuse them with a canonical correlation analysis and same procedure also done using LBP and HOG. The SIFT features have some limitations they don’t work well with lighting changes, quite slow, and mathematically complicated and computationally heavy. The combinations of HOG and LBP features make the system robust against some variations like illumination and expressions. Also, face recognition technique used a different classifier to extract the useful information from images to solve the problems. This paper is organized into four sections. Introduction in the first section. The second section describes feature descriptors and the third section describes proposed methods, final sections describes experiments result and conclusion phase.
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.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Identifying Gender from Facial Parts Using Support Vector Machine ClassifierEditor IJCATR
Gender classification can be stated as inferring female or male from a collection of facial images. There exist different
methods for gender classification, such as gait, iris, hand shape and hair, it is probably better way to find out gender based on facial
features. In this paper SVM basic kernel function has been employed firstly to detect and classify the human gender Image into
two labels i.e. (1) male and (2) female. The gender classifier achieves over 96% accuracy.
Laboratory Investigation of Conventional Asphalt Mix Using Shell Thiopave for...IJERA Editor
The characteristic performance of asphalt pavement always depends on the properties of bitumen, volumetric properties of asphalt mixtures. Bitumen is visco– elastic material where the temperature and rate of load application have a great influence on its behavior. There are different solutions to reduce the pavement distress such as using Thiopave (binder extender and asphalt mixture modifier) in the mix design. Thiopave can significantly alter the performance properties of the mix and it is helpful to extend the life span of pavement. In this study, investigating use of thiopave and the change in the performance properties is dependent both on the percentage of virgin binder using VG-30 bitumen that is substituted with thiopave with different percentages. The study indicated that 10%, 20%, 30% and 40% replacement of binder was done with thiopave. The most notable impact of the addition of thiopave to a bituminous mixture is an increase in the stiffness of the mixture for better resistance to fatigue cracking and rutting. Thiopave materials can have a positive impact on laboratory mixture performance. The addition of thiopave has been shown to significantly increase Marshall Stability. From this study it is observed that thiopave can be utilized up to 30% to 40% as replacement to bitumen.
Provable data possession for securing the data from untrusted serverIJERA Editor
The model described for the use of Provable data Possession which allow the client to access the stored data at
an Untrusted server that the server possesses the original data without retrieving it. This model executes the
probabilistic proof of possession by random set of blocks which is derived from the server that dramatically
reduces the cost of I/O. Sometimes the Client maintenance the constant amount of data which is used to verify
the proof. The response protocol can transmit a small amount of data, which can minimize network
communication. The two provably –Securer PDP Schemes presents more efficient schemes than previous
solution .Even when compared with schemes that achieve weaker guarantees. It is the widely distributed storage
systems. Using the experiment we can implement and verify the practicality of PDP and we can revel that the
performance of the PDP that is bounded by disk I/O and that cannot be determined by computation.
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
Human Face Detection Systemin ANew AlgorithmIOSRjournaljce
Trying to detecting a face from any photo is big problem and got these days a focusing because of it importance, in face recognition system,face detection in one of the basic components. A lot of troubles are there to be solved in order to create a successful face detection algorithm. The skin of face has its properties in color domain and also a texture which may help in the algorithm for detecting faces because of its ability to find skins from photo. Here we are going to create a new algorithm for human face detection depending on skin color tone specially YCbCr color tone as an approach to slice the photo into parts. In addition, Gray level has been used to detect the area which contains a skin, after that anotherlevel used to erase the area that does not contain skin. The system which proposed applied on many photos and passed with great accuracy of detecting faces and it has a good efficient especially to separate the area that does not contain skin or face from the area which contain face and skin. It has been agreed and approved that the accuracy of the proposed system is 98% in human face detection.
50Combining Color Spaces for Human Skin Detection in Color Images using Skin ...idescitation
Skin detection remains a challenging task over
several decades in spite of many techniques evolved. It is the
elementary step of most of the computer vision applications
like face recognition, human computer interaction, etc. It
depends on the suitability of color space chosen, skin modeling
and classification of skin and non-skin pixels under varying
illumination conditions. This paper presents a symbolic
interpretation on the performance of the color spaces using
piecewise linear decision boundary classifier in color images
to find the winning color space (s). The whole task is divided
into three processes: analysis of color spaces individually;
analysis of the combination of two color spaces; and finally
making a comparative analysis among the results obtained by
the above two processes. For performing the fair evaluation,
the whole experiment is tested over commonly used databases.
Based on the success rate, false positive and false negative of
each color spaces, the winner(s) has been chosen among single
and the combination of color spaces.
A novel approach for performance parameter estimation of face recognition bas...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Hand gesture recognition system has received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. In this work Hand segmentation using color models is introduced for obtaining hand gestures or detecting user’s hand by color segmentation technique for faster, better, robust, accurate and real-time applications. There are many such color models available for human hand and human skin detection with relative advantages and disadvantages in the field of Image Processing. For the purpose of hand Segmentation mix model approach has been adopted for best results. For detection of Hand from an image. The proposed approach is found to be accurate and effective for multiple conditions
Recent developments in paper currency recognition systemeSAT Journals
Abstract
Currency denomination recognition is one the active research topics at present. And this wide interest is particularly due to the
various potential applications it has. Monetary transaction is an integral part of our day to day activities. However, blind people
particularly suffer in monetary transactions. They are not able to effectively distinguish between various denominations and are often
deceived by other people. Also, a reliable currency recognition system could be used in any sector wherever monetary transaction is
of concern. Thus, there is an ardent need to design a system that is helpful in recognition of paper currency notes correctly. Currency
denomination detection is a vast area of research and significant progress had been achieved over the years. This paper presents an
extensive survey of research on various developments in recent years in identification of currency denomination. A number of
techniques applied by various researchers are discussed briefly in order to assess the state of art.
Keywords:- Currency recognition system, Artificial Neural Network
Feature extraction is becoming popular in face recognition method. Face recognition is the interesting and growing area in real time applications. In last decades many of face recognitions methods has been developed. Feature extraction is the one of the emerging technique in the face recognition methods. In this method an attempt to show best faces recognition method. Here used different descriptors combination like LBP and SIFT, LBP and HOG for feature extraction. Using a single descriptor is difficult to address all variations so combining multiple features in common. Find LBP and SIFT features separately from the images and fuse them with a canonical correlation analysis and same procedure also done using LBP and HOG. The SIFT features have some limitations they don’t work well with lighting changes, quite slow, and mathematically complicated and computationally heavy. The combinations of HOG and LBP features make the system robust against some variations like illumination and expressions. Also, face recognition technique used a different classifier to extract the useful information from images to solve the problems. This paper is organized into four sections. Introduction in the first section. The second section describes feature descriptors and the third section describes proposed methods, final sections describes experiments result and conclusion phase.
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.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Identifying Gender from Facial Parts Using Support Vector Machine ClassifierEditor IJCATR
Gender classification can be stated as inferring female or male from a collection of facial images. There exist different
methods for gender classification, such as gait, iris, hand shape and hair, it is probably better way to find out gender based on facial
features. In this paper SVM basic kernel function has been employed firstly to detect and classify the human gender Image into
two labels i.e. (1) male and (2) female. The gender classifier achieves over 96% accuracy.
Laboratory Investigation of Conventional Asphalt Mix Using Shell Thiopave for...IJERA Editor
The characteristic performance of asphalt pavement always depends on the properties of bitumen, volumetric properties of asphalt mixtures. Bitumen is visco– elastic material where the temperature and rate of load application have a great influence on its behavior. There are different solutions to reduce the pavement distress such as using Thiopave (binder extender and asphalt mixture modifier) in the mix design. Thiopave can significantly alter the performance properties of the mix and it is helpful to extend the life span of pavement. In this study, investigating use of thiopave and the change in the performance properties is dependent both on the percentage of virgin binder using VG-30 bitumen that is substituted with thiopave with different percentages. The study indicated that 10%, 20%, 30% and 40% replacement of binder was done with thiopave. The most notable impact of the addition of thiopave to a bituminous mixture is an increase in the stiffness of the mixture for better resistance to fatigue cracking and rutting. Thiopave materials can have a positive impact on laboratory mixture performance. The addition of thiopave has been shown to significantly increase Marshall Stability. From this study it is observed that thiopave can be utilized up to 30% to 40% as replacement to bitumen.
Provable data possession for securing the data from untrusted serverIJERA Editor
The model described for the use of Provable data Possession which allow the client to access the stored data at
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solution .Even when compared with schemes that achieve weaker guarantees. It is the widely distributed storage
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Door Misha de Sterke
van De Sterke Consultancy
Volg me via @mishadesterke
Meer informatie? Ga naar www.mishadesterke.nl of mail naar desterkeconsultancy@gmail.com
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patches. Several theoretical analysis techniques are summarized. Practical procedures are given for both
standard rectangular and circular patches. The quality, bandwidth, and efficiency factors of typical patch designs
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to be understood so that suitable configuration can be chosen for various applications
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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.
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Environmental Engineering,
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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
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Similar to Region based elimination of noise pixels towards optimized classifier models for skin pixel detection (20)
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
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When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
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and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
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Region based elimination of noise pixels towards optimized classifier models for skin pixel detection
1. Gagandeep Kaur et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 3, ( Part -1) March 2015, pp.173-178
www.ijera.com 173 | P a g e
Region based elimination of noise pixels towards optimized
classifier models for skin pixel detection
Gagandeep Kaur*, Seema Pahwa**, Kushagra Sharma***
*(Department of IT, Sri Sukhmani College of Engineering, & Technology, Derabassi- Punjab Technical
University)
** (Department of IT, Sri Sukhmani College of Engineering, & Technology, Derabassi - Punjab Technical
University)
*** (Department of Computer Science and Engineering, Sri Sukhmani College of Engineering, & Technology,
Derabassi - Punjab Technical University)
ABSTRACT
The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin
segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is
typically used as a pre-processing step to extract the face regions from human image. In past, there are several
computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin
detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then
skin classifier model have been applied to label the pixel into skin or non-skin regions.
Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models
for skin pixel detection applied on human images” would be performed which involve the process of image
representation in color models, elimination of non-skin pixels in the image, and then pre-processing and
cleansing of the collected data, feature selection of the human image and then building the model for classifier.
In this research and implementation of skin pixels classifier models are proposed with their comparative
performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human
image or stack of human images. The performance is evaluated by comparing and analysing skin colour
segmentation algorithms. During the course of research implementation, efforts are iterative which help in
selection of optimized skin classifier based on the machine learning algorithms and their performance analysis.
Keywords-Digital Image Processing, Data Mining, Digital Image Processing, Skin Pixel Detection, Color
Models, WEKA
I. INTRODUCTION
The identification and determination of the face
in a human image is called face detection. Face
detection is a research problem to select the sub-
window of human image which contains a face. In
computer vision, there are various applications such
as face processing. Several applications, such as face
processing, computer human interaction, human
crowd surveillance, biometric, video surveillance,
artificial intelligence and content-based image
retrieval [1]. Face detection is a pre-processing step
in all these application which is used to obtain the
“object” related to the face region. In the form of face
detection, the proposed processes in research can be
defined as shown in the following figure 1.
In research, there are many techniques are
proposed for pre-identification of the face in a human
image which start from the phase of skin color
segmentation. The skin color segmentation is
representing the human image in a color models. In
computer vision, there are many color models to
represent the image; these are RGB, YCbCr, HSV,
and LAB color models. Next step is to convert the
skin image into binary image which contains the skin
regions. Then next step in face detection is to remove
non human face area from the segmented image with
the help of the Knowledge-based methods or human
face features [1]. Here in this research, the
elimination of non-skin region from the skin image
and selection of skin part with the help of
representation of human in particular color model
such as RGB and HSV is presented. Then machine
learning algorithms are applied to build an optimized
skin classifier on the segmented image.
Skin Segmentation based on Color Models:
To represent the human image, color model is a
useful piece of information which decide the
optimality and efficiency of the skin segmentation.
For skin detection and segmentation, the color model
is important and useful parameter [2]. Like, in terms
of face detection, based on the appropriate color
model representation, the extensive search of human
RESEARCH ARTICLE OPEN ACCESS
2. Gagandeep Kaur et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 3, ( Part -1) March 2015, pp.173-178
www.ijera.com 174 | P a g e
face pixels can be avoided which help in deciding the
human image region related to face.
Figure 1: Face Detection approach
In this step, we describe that how non skin color
is rejected from an Image so that the image may
contains only skin like areas, which will be our skin
color segmented image for further processing. From
different type of color models, in HSV color model,
Hue (H) is not reliable for the discrimination task
when the saturation is low, Also in YCbCr color
model, the distribution of skin areas is consistent
across different races in the Cb and Cr color spaces,
the RGB color model is lighting sensitive so
Therefore, when we use different color models under
uncontrolled conditions, and we get consequently
result for skin color detection. The accuracy of skin
detection depends on both the color model and the
method of skin pixels classification or detection.
Hence, the challenge problem is how to select color
models that are suitable for skin pixel classifications
under different varying conditions. In this thesis,
there are four color models are used for skin color
segmentation or detection of skin pixels. These are
RGB, YCbCr, and HSV and CIELAB color models.
The combination of these color models overcomes all
varying lighting conditions and changes in
illumination, and it gives better result than individual
color model result [3]. Figure 2 shows the
combination of skin color segmentation.
RGB YCbCr HSV CEIL*a*b* Final Skin Color
Segmented Image Binary Image Input image
Fig 2: Skin color segmentation
3. BINARY IMAGE FORMATION
As stated above, the segmented skin image is
only contained the skin areas or skin regions but there
is no information which region or which area is
having human face or part of human skin, thereby we
need to send this image for further classification
stage which reject the non-human face of the image
and select the part of the image which match with the
human face. The similar principle is working on the
skin detection which is majorly divided into two
broad categories as skin region selection which is
basically the elimination of non-skin regions and
other is building the classification model to select
only skin related pixels.
II. Skin Classifier Model
A. Scope and Objectives
The research and development involved in this
research include:
Literature Survey related to skin detection.
Proof-of-Concept of Skin Pixel Detection
Elimination of non-skin based on region based
selection
Data pre-processing and feature selection of
human image.
o Feature selection.
o Classification
o Model Creation
Development of optimized skin classifier based
on multiple iterations.
Experimental Results Illustrations and future
scope.
Methodology Used:
3. Gagandeep Kaur et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 3, ( Part -1) March 2015, pp.173-178
www.ijera.com 175 | P a g e
Figure 3: Flowchart and Algorithm
Tools and Techniques:
o Weka:
o ImageJ aka Fiji
ImageJ aka Fiji:
ImajeJ: Fiji is an image processing package. It
can be described as a distribution
of ImageJ (and ImageJ2 together with Java, Java3D
and a lot of plugins organized into a coherent menu
structure. Fiji compares to ImageJ as Ubuntu
compares to Linux.
Weka supports several standard data
mining tasks, more specifically, data pre-
processing, clustering, classification, regression,
visualization, and feature selection.
The main goal of this plugin is to work as
a bridge between the Machine Learning and the
Image processing fields. It provides the framework to
use and, more important, compare any available
classifier to perform image segmentation based on
pixel classification.
1. Data Collection: First of all the data will be
collected related to human images and
segregated from different sources.
2. Data Filtering and Cleansing: The data we have
collected are not clean and may contain errors,
missing values, noisy or inconsistent data. So we
need to apply different techniques to get rid of
such anomalies.
3. Feature Selection: Features will be selected
based on human images which related to pixels
of the images.
4. Training the model/model creation: The model
will be created and trained based on the training
data-set.
5. Testing the model: Testing of the model based
on supplied test data
6. Evaluation: Performance evaluation of the
developed model.
Modules of Implementation:
1. Pre-processing: In this step, the data from
various sources related to human image will be
collected and pre-process to fuse it in the format
of Weka tool which is .arff format. When a color
image is given as an input to the algorithm, the
first step is pre-processing. This step is mainly
concerned with the image conversion and
cropping.
2. Feature Extraction: The data set collected will
further be processed to select the relevant
features of the human image. Feature extraction
transforms the input data into the set of features
Feature extraction is concerned with recovering
the defining attributes obscured by imperfect
measurements.
3. Classification: For classification, we train the
machine with the help of learning algorithms.
4. Model Creation: Finally the accurate model will
be created to test and cross-validate the results.
Figure 2: Modules of implementation
Machine learning algorithms implemented and
compared to build optimized classifier model:
1. Naive Bayes Classifier: It is a simple
probabilistic model of machine learning which is
used to develop a classifier.
2. Decision Tree: It is basically a predictive model
of machine learning subject.
4. Gagandeep Kaur et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 3, ( Part -1) March 2015, pp.173-178
www.ijera.com 176 | P a g e
3. SMO: Sequential minimal optimization (SMO)
is an algorithm for solving the optimization
problem.
4. J48: C4.5 is an algorithm used to generate
a decision tree developed by Ross Quinlan. C4.5
is an extension of Quinlan's earlier ID3
algorithm. The decision trees generated by C4.5
can be used for classification, and for this reason,
C4.5 is often referred to as a statistical classifier.
5. ZeroR: ZeroR is the simplest classification
method which relies on the target and ignores all
predictors. ZeroR classifier simply predicts the
majority category (class). Although there is no
predictability power in ZeroR, it is useful for
determining a baseline performance as a
benchmark for other classification methods.
Experimental Results:
Algorith
m
TP FP Precisio
n
Recal
l
RO
C
area
Decision
Tree
0.94
9
0.20
5
0.948 0.949 0.91
7
Bayesian
Logistic
Regressio
n
0.93
8
0.25
1
0.936 0.938 0.84
3
J48 0.95
8
0.18
7
0.957 0.958 0.94
9
Table 1: Comparison of classifier models
Classification Rates: (Total instances=2067)
Algorithm Correctly
classified
instances
Incorrectly
classified
instances
Decision
Tree
1962
(94.9202%)
105(5.0798%)
Bayesian
Logistic
Regression
1938(93.7591%) 129(6.2409%)
J48 1980(95.791%) 87 (4.209%)
Table 2: Classifications rates of classifiers
Confusion Matrix (Decision Tree):
a b -------classified as
1748 47 a=class 1
58 194 b= class 2
Confusion Matrix (Bayesian Logistic Regression):
a b -------classified as
1757 58 a=class 1
71 181 b= class 2
Confusion Matrix (J48):
a b -------classified as
1781 34 a=class 1
53 199 b= class 2
ROC curves:
1. Decision Tree
Figure 3: ROC curve of Decision Tree
2. Bayesian Logistic Regression
Figure 4: ROC curve of Bayesian Logistic
Regression
3. J48 algorithm
Figure 5: ROC curve of J48
III. CONCLUSION
Here in this research, the machine learning based
approach to build the skin classifier is being
presented. The iterative mechanism is implemented
to build the optimized skin classifiers. First step in
the research is elimination of noise pixels and
selection of skin-regions or skin areas which is
5. Gagandeep Kaur et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 3, ( Part -1) March 2015, pp.173-178
www.ijera.com 177 | P a g e
further send to the second phase to process it further.
The features which are taken for skin classifier need
to be optimized in the future.
IV. ACKNOWLEDGEMENTS
We would like to sincerely thankful to respected
Seema Pahwa, Assistant Professor, Department of IT,
Sri Sukhmani College of Engineering and
Technology, Derabassi, under Punjab Technical
University, for her contribution and help in writing
this paper.. We would also thankful to our team-
mates and all my friends who involved in the
discussions and deliberations during the
implementation and development aspects.
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