In the domain of image processing, face recognition is one of the most well-known research field. When
humans have very similar biometric properties, such as identical twins, the face recognition system is
considered as a challengeable problem. In this paper, the AdaBoost method is utilized to detect the
facial area of input image.
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.
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
A novel approach for efficient skull stripping using morphological reconstruc...eSAT Journals
Abstract Brain is the part of the central nervous system located in skull. For the diagnosis of human brain bearing tumour, skull stripping plays an important pre-processing role. Skull stripping is the process separating brain and non-brain tissues of the head which is the critical processing step in the analysis of neuroimaging data. Though various algorithms have been proposed to address this problem, challenges remain. In this paper a new efficient skull stripping method for magnetic resonance images (MRI) is proposed. This method adopts a two-step approach; in the first step an improved systematic application of morphological reconstructions operations is done for the brain image and in the second step, a thresholding based technique is used to extract the brain inside the skull. This paper experimented on Axial PD and FLAIR MRI brain images. Index Terms: Skull stripping, thresholding, morphological reconstruction, Axial PD and FLAIR MRI images of brain.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
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.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
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
A novel approach for efficient skull stripping using morphological reconstruc...eSAT Journals
Abstract Brain is the part of the central nervous system located in skull. For the diagnosis of human brain bearing tumour, skull stripping plays an important pre-processing role. Skull stripping is the process separating brain and non-brain tissues of the head which is the critical processing step in the analysis of neuroimaging data. Though various algorithms have been proposed to address this problem, challenges remain. In this paper a new efficient skull stripping method for magnetic resonance images (MRI) is proposed. This method adopts a two-step approach; in the first step an improved systematic application of morphological reconstructions operations is done for the brain image and in the second step, a thresholding based technique is used to extract the brain inside the skull. This paper experimented on Axial PD and FLAIR MRI brain images. Index Terms: Skull stripping, thresholding, morphological reconstruction, Axial PD and FLAIR MRI images of brain.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
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.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Supervised Blood Vessel Segmentation in Retinal Images Using Gray level and M...IJTET Journal
The segmentation of membranel blood vessels within the retina may be a essential step in designation of diabetic retinopathy during this paper, gift a replacement methodology for mechanically segmenting blood vessels in retinal pictures. 2 techniques for segmenting retinal blood vessels, supported totally different image process techniques, square measure represented and their strengths and weaknesses square measure compared. This methodology uses a neural network (NN) theme for element classification and gray-level and moment invariants-based options for element illustration. The performance of every algorithmic program was tested on the STARE and DRIVE dataset. wide used for this purpose, since they contain retinal pictures and also the
vascular structures. Performance on each sets of check pictures is healthier than different existing pictures. The methodology
proves particularly correct for vessel detection in STARE pictures. This effectiveness and lustiness with totally different image conditions, is employed for simplicity and quick implementation. This methodology used for early detection of Diabetic Retinopathy (DR)
Automatic rectification of perspective distortion from a single image using p...ijcsa
Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the distortion of a single image without losing any desired information is one of the challenging task in the field of Computer Vision. We consider the problem of estimating perspective distortion from a single still image of an unstructured environment and to make perspective correction which is both quantitatively accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A method based on plane homography and transformation is used to make perspective correction. The algorithm infers frontier information directly from the images, without any reference objects or prior knowledge of the camera parameters. The frontiers are detected using geometric context based segmentation. The goal of this paper is to present a framework providing fully automatic and fast perspective correction.
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
A NOVEL IMAGE SEGMENTATION ENHANCEMENT TECHNIQUE BASED ON ACTIVE CONTOUR AND...acijjournal
Topological alignments and snakes are used in image processing, particularly in locating object
boundaries. Both of them have their own advantages and limitations. To improve the overall image
boundary detection system, we focused on developing a novel algorithm for image processing. The
algorithm we propose to develop will based on the active contour method in conjunction with topological
alignments method to enhance the image detection approach. The algorithm presents novel technique to
incorporate the advantages of both Topological Alignments and snakes. Where the initial segmentation
by Topological Alignments is firstly transformed into the input of the snake model and begins its
evolvement to the interested object boundary. The results show that the algorithm can deal with low
contrast images and shape cells, demonstrate the segmentation accuracy under weak image boundaries,
which responsible for lacking accuracy in image detecting techniques. We have achieved better
segmentation and boundary detecting for the image, also the ability of the system to improve the low
contrast and deal with over and under segmentation.
One-Sample Face Recognition Using HMM Model of Fiducial AreasCSCJournals
In most real world applications, multiple image samples of individuals are not easy to collate for direct implementation of recognition or verification systems. Therefore there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved.
Content Based Image Retrieval Approach Based on Top-Hat Transform And Modifie...cscpconf
In this paper a robust approach is proposed for content based image retrieval (CBIR) using texture analysis techniques. The proposed approach includes three main steps. In the first one, shape detection is done based on Top-Hat transform to detect and crop object part of the image. Second step is included a texture feature representation algorithm using color local binary patterns (CLBP) and local variance features. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Low noise sensitivity, rotation invariant, shift invariant, gray scale invariant and low computational complexity are some of other advantages.
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.
Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Lo...IJECEIAES
The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: firstly, both Gabor filters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
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
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The 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 geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled 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.
AN EFFICIENT FEATURE EXTRACTION METHOD WITH LOCAL REGION ZERNIKE MOMENT FOR F...ieijjournal
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. The face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, the facial area in an image is detected using AdaBoost approach. After that the facial area is divided into some local regions. Finally, new efficient facial-based identical twins feature extractor based on the geometric moment is applied into local regions of face image.The utilized geometric moment is Zernike Moment (ZM) as a feature extractor inside the local regions of facial area of identical twins images. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled 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.
An Efficient Feature Extraction Method With Local Region Zernike Moment for F...ieijjournal
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. The face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, the facial area in an image is detected using AdaBoost approach. After that the facial area is divided into some local regions. Finally, new efficient facial-based identical twins feature extractor based on the geometric moment is applied into local regions of face image.The utilized geometric moment is Zernike Moment (ZM) as a feature extractor inside the local regions of facial area of identical twins images. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled 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.
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The 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 geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled 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.
Supervised Blood Vessel Segmentation in Retinal Images Using Gray level and M...IJTET Journal
The segmentation of membranel blood vessels within the retina may be a essential step in designation of diabetic retinopathy during this paper, gift a replacement methodology for mechanically segmenting blood vessels in retinal pictures. 2 techniques for segmenting retinal blood vessels, supported totally different image process techniques, square measure represented and their strengths and weaknesses square measure compared. This methodology uses a neural network (NN) theme for element classification and gray-level and moment invariants-based options for element illustration. The performance of every algorithmic program was tested on the STARE and DRIVE dataset. wide used for this purpose, since they contain retinal pictures and also the
vascular structures. Performance on each sets of check pictures is healthier than different existing pictures. The methodology
proves particularly correct for vessel detection in STARE pictures. This effectiveness and lustiness with totally different image conditions, is employed for simplicity and quick implementation. This methodology used for early detection of Diabetic Retinopathy (DR)
Automatic rectification of perspective distortion from a single image using p...ijcsa
Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the distortion of a single image without losing any desired information is one of the challenging task in the field of Computer Vision. We consider the problem of estimating perspective distortion from a single still image of an unstructured environment and to make perspective correction which is both quantitatively accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A method based on plane homography and transformation is used to make perspective correction. The algorithm infers frontier information directly from the images, without any reference objects or prior knowledge of the camera parameters. The frontiers are detected using geometric context based segmentation. The goal of this paper is to present a framework providing fully automatic and fast perspective correction.
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
A NOVEL IMAGE SEGMENTATION ENHANCEMENT TECHNIQUE BASED ON ACTIVE CONTOUR AND...acijjournal
Topological alignments and snakes are used in image processing, particularly in locating object
boundaries. Both of them have their own advantages and limitations. To improve the overall image
boundary detection system, we focused on developing a novel algorithm for image processing. The
algorithm we propose to develop will based on the active contour method in conjunction with topological
alignments method to enhance the image detection approach. The algorithm presents novel technique to
incorporate the advantages of both Topological Alignments and snakes. Where the initial segmentation
by Topological Alignments is firstly transformed into the input of the snake model and begins its
evolvement to the interested object boundary. The results show that the algorithm can deal with low
contrast images and shape cells, demonstrate the segmentation accuracy under weak image boundaries,
which responsible for lacking accuracy in image detecting techniques. We have achieved better
segmentation and boundary detecting for the image, also the ability of the system to improve the low
contrast and deal with over and under segmentation.
One-Sample Face Recognition Using HMM Model of Fiducial AreasCSCJournals
In most real world applications, multiple image samples of individuals are not easy to collate for direct implementation of recognition or verification systems. Therefore there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved.
Content Based Image Retrieval Approach Based on Top-Hat Transform And Modifie...cscpconf
In this paper a robust approach is proposed for content based image retrieval (CBIR) using texture analysis techniques. The proposed approach includes three main steps. In the first one, shape detection is done based on Top-Hat transform to detect and crop object part of the image. Second step is included a texture feature representation algorithm using color local binary patterns (CLBP) and local variance features. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Low noise sensitivity, rotation invariant, shift invariant, gray scale invariant and low computational complexity are some of other advantages.
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.
Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Lo...IJECEIAES
The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: firstly, both Gabor filters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
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
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The 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 geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled 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.
AN EFFICIENT FEATURE EXTRACTION METHOD WITH LOCAL REGION ZERNIKE MOMENT FOR F...ieijjournal
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. The face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, the facial area in an image is detected using AdaBoost approach. After that the facial area is divided into some local regions. Finally, new efficient facial-based identical twins feature extractor based on the geometric moment is applied into local regions of face image.The utilized geometric moment is Zernike Moment (ZM) as a feature extractor inside the local regions of facial area of identical twins images. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled 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.
An Efficient Feature Extraction Method With Local Region Zernike Moment for F...ieijjournal
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. The face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, the facial area in an image is detected using AdaBoost approach. After that the facial area is divided into some local regions. Finally, new efficient facial-based identical twins feature extractor based on the geometric moment is applied into local regions of face image.The utilized geometric moment is Zernike Moment (ZM) as a feature extractor inside the local regions of facial area of identical twins images. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled 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.
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The 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 geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled 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.
& Topics International Journal of Computer Science, Engineering and Informati...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The 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 geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled 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.
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
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Multimodal Approach for Face Recognition using 3D-2D Face Feature FusionCSCJournals
3D Face recognition has been an area of interest among researchers for the past few decades especially in pattern recognition. The main advantage of 3D Face recognition is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person identification using 3D Face data. Use of unregistered 3D Face data for feature extraction significantly increases the operational speed of the system with huge database enrollment. In this work, unregistered 3D Face data is fed to a classifier in multiple spectral representations of the same data. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used for the spectral representations. The face recognition accuracy obtained when the feature extractors are used individually is evaluated. The use of depth information alone in different spectral representation was not sufficient to increase the recognition rate. So a fusion of texture and depth information of face is proposed. Fusion of the matching scores proves that the recognition accuracy can be improved significantly by fusion of scores of multiple representations. FRAV3D database is used for testing the algorithm.
Realtime human face tracking and recognition system on uncontrolled environmentIJECEIAES
Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer.
SYMMETRICAL WEIGHTED SUBSPACE HOLISTIC APPROACH FOR EXPRESSION RECOGNITIONijcsit
Human face expression is one of the cognitive activity or attribute to deliver the opinions to others.This paper mainly delivers the performance of appearance based holistic approach subspace methods based on Principal Component Analysis (PCA). In this work texture features are extracted from face images using Gabor filter. It was observed that extracted texture feature vector space has higher dimensional and has
more number of redundant contents. Hence training, testing and classification time becomes more. The expression recognition accuracy rate is also reduced. To overcome this problem Symmetrical Weighted 2DPCA (SW2DPCA) subspace method is introduced. Extracted feature vector space is projected in to subspace by using SW2DPCA method. By implementing weighted principles on odd and even symmetrical
decomposition space of training samples sets proposed method have been formed. Conventional PCA and 2DPCA method yields less recognition rate due to larger variations in expressions and light due to more number of feature space redundant variants. Proposed SW2DPCA method optimizes this problem by reducing redundant contents and discarding unequal variants. In this work a well known JAFFE databases
is used for experiments and tested with proposed SW2DPCA algorithm. From the experimental results it was found that facial recognition accuracy rate of GF+SW2DPCA based feature fusion subspace method has been increased to 95.24% compared to 2DPCA method.
Combination of texture feature extraction and forward selection for one-clas...IJECEIAES
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This project is to retrieve the similar geographic images from the dataset based on the features extracted.
Retrieval is the process of collecting the relevant images from the dataset which contains more number of
images. Initially the preprocessing step is performed in order to remove noise occurred in input image with
the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant
Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the
features from the images. After this process, the relevant geographic images are retrieved from the dataset
by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which
are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to
perform reliable recognition, it is important that the feature extracted from the training image be
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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.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
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Face Recognition System using Self Organizing Feature Map and Appearance Base...ijtsrd
Face Recognition has develop one of the most effective presentations of image analysis. This area of research is important not only for the applications in human computer interaction, biometric and security but also in other pattern classification problem. To improve face recognition in this system, two methods are used PCA Principal component analysis and SOM Self organizing feature Map .PCA is a subspace projection method is used compress the input face image. SOM method is used to classify DCT based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. The aim of this system is that input image has to compare with stored images in the database using PCA and SOM method. An image database of 100 face images is evaluated containing 10 subjects and each subject having 10 images with different facial expression. This system is evaluated by measuring the accuracy of recognition rate. This system has been implemented by MATLAB programming. Thaung Yin | Khin Moh Moh Than | Win Tun "Face Recognition System using Self-Organizing Feature Map and Appearance-Based Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26691.pdfPaper URL: https://www.ijtsrd.com/computer-science/cognitive-science/26691/face-recognition-system-using-self-organizing-feature-map-and-appearance-based-approach/thaung-yin
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LOCAL REGION PSEUDO-ZERNIKE MOMENT- BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL TWINS
1. Advanced Computational Intelligence: An International Journal (ACII),Vol.1, No.1, July 2014
19
LOCAL REGION PSEUDO-ZERNIKE
MOMENT- BASED FEATURE EXTRACTION
FOR FACIAL RECOGNITION OF IDENTICAL
TWINS
Zahra Ahmadi-Dastjerdi1 and Karim Faez2
1Department of Computer Engineering, Qazvin Branch, Islamic Azad University, Qazvin,
Iran
2
Department of Electrical Engineering, Amirkabir University of Technology, Tehran,
Iran
ABSTRACT
In the domain of image processing, face recognition is one of the most well-known research field. When
humans have very similar biometric properties, such as identical twins, the face recognition system is
considered as a challengeable problem. In this paper, the AdaBoost method is utilized to detect the
facial area of input image. After that the facial area is divided into some local regions. Finally, new
efficient facial-based identical twins feature extractor based on the geometric moment is applied into
local regions of face image. The used feature extractor is Pseudo-Zernike Moment (PZM) which is
employed inside the local regions of facial area of identical twins images. To evaluate the proposed
method, two datasets, Twins Days Festival and Iranian Twin Society, are collected where the datasets
includes scaled and rotated facial images of identical twins in different illuminations. The experimental
results demonstrates the ability of proposed method to recognize a pair of identical twins in
different situations such as rotation, scaling and changing illumination
KEYWORDS
Face Recognition, Local Regions, Identical Twins, Invariant Moment, Pseudo-Zernike
Moment
1. INTRODUCTION
Face is one of the best features to identify people identity from his (her) image. According
to this property, identification of facial of identical twins is critical because of the
similarity between a pair of twin. To identify identical twins, some important previous works
are listed as: in [12], the proposed face detection method contains three levels: (1) overall
appearance of the face is constructed; (2) exact geometric and structural embedment of face
with differentiating between two similar faces are performed; and (3) the third level consists
of process of skin disorders detection such as wounds. Sun et al. [16] utilized Cognitec
FaceVACS system to recognize identical twins from CASIA Multimodal Biometrics
Database. They obtained true accept rate of approximately 90% at a false accept rate greater
than 10%. In [14], the proposed fce detection method includes tree steps: (1) the proposed
method marks the face images using normal geometric methods; (2), the Euclidean distance
between a pair of markers of test image and images in dataset, are computed and compared;
and (3), the algorithm involves finding the strong similarity between the marked regions.
2. Advanced Computational Intelligence: An International Journal (ACII),Vol.1, No.1, July 2014
20
In [15], recognition of identical twins is performed using marks on the face image.
Martin et al. [3] employed DNA approach to recognize identical twins. In this paper, the
geometric moments is used to extract feature vector from input facial images of twins in order
to recognize identical twins.
This paper is organized as follows: in Section 2, feature extraction step of a face recognition
system is introduced. In Section 3, the proposed method is demonstrated. In Section 4,
experimental results are presented and the paper will be concluded in Section 5.
2. FEATURE EXTRACTION
Each face detection system contains four steps: preprocessing, face localization, feature
extraction and classification. Feature extraction is a process which is employed to collect
useful information from raw data and so this process is necessary for the pattern recognition
problems. Since the feature extraction methods are not public and depend on application,
feature extraction step may show different results. There are two different groups for the
feature extraction methods: structural features and statistical features [11][19]. In the first
group, local structure of input image takes into account where these structural features deal
with local data [7].
In the second group, the statistics-based feature extraction methods create a set of feature
vector according to the global data of input image. Extraction of irrelevant information from
facial image may create unappreciated set of feature vector elements such as hair, shoulders,
and background should be regarded in the feature extraction phase [10]. Some of statistics-
based feature extraction methods are listed as Principle Component Analysis (PCA),
Legendre Moment (LM) [13] and Zernike Moments (ZM) [20], Pseudo-Zernike Moment
(PZM) [8]. Legendre functions are Legendre differential equation. It is important that
Legendre moments are orthogonal, independent of each other and free of data redundancy.
Zernike Moment (ZM) is a set of orthogonal polynomials which are defined into a unit
disk. The ZM technique is independent of scale and rotation of face in image. The Pseudo-
Zernike Moment (PZM) is the similar to ZM but the feature vector elements extracted by
PZM is more than the feature vector extracted by ZM and so is more suitable than ZM for
recognition of identical twins. In this study, PZM is utilized to extract feature vector in order
to recognize identical twins. The PZM will be described in the next Section.
3. PROPOSED METHOD
In this paper, the goal is distinguishing of identical twins using facial image. For this purpose,
at the first step, a boosting method named AdaBoost [18] technique is employed to localize
the facial area of input image and also create subimge. In the next step, the PZM is applied on
each subimage to create feature vector elements for each subimage and this step is performed
for all images in dataset. In the final step, the feature vectors of test image is compared to
feature vectors of all images in dataset and the image of dataset with the minimum distance to
the test image is selected as the pair of test image. In the next Section, the processes of face
detection and feature extraction will be described in detail.
3. Advanced Computational Intelligence: An International Journal (ACII),Vol.1, No.1, July 2014
21
3.1. Face Detection Method
In the process of identical twins recognition, the second step is face detection. In the proposed
method, mixture of successively more complex classifiers in a cascade structure using
AdaBoost [18] is applied to detect facial area of input image using a small number of Haar-
like features [18]. After finding the facial area, an ellipse will be drowning around the main
location of face [8]. The ellipse shape includes five parameters: X0 and Y0 are the centers of the
ellipse, θ is the orientation, α and β are the minor and the major axes of the ellipse,
respectively.
Before the computation of the above mentioned parameters, geometric moments should be
described. The geometric moments of order p+q of a digital image are defined as
where p, q = 0, 1, 2, … and f(x, y) is the grey-scale value of the digital image at x and y
location. The translation invariant central moments are achieved by placing origin at the center
of the image:
4. Advanced Computational Intelligence: An International Journal (ACII),Vol.1, No.1, July 2014
22
3.2. Pseudo-Zernike Moment (PZM)
PZM is based on the geometric moment and used to create feature vector according to the
global data of an image [9]. The advantages of PZM is that its orthogonal moments are shift,
rotation, and scale invariants which are suitable for pattern recognition problems
[6][5][8][17]. Also, PZM includes several orthogonal sets of complex-valued polynomials
defined as
3.3 Creating feature vector
In this step, PZM is applied on each subimage to extract feature vector. For this purpose,
the feature vector elements are defined as:
3.4 Local Regional Pseudo-Zernike Moment (LRPZM)
The main contribution of this paper is focused on applying of PZM in a local area. For this
purpose, the obtained subimage from AdaBoost is divided into 13 subregions including an eye
5. Advanced Computational Intelligence: An International Journal (ACII),Vol.1, No.1, July 2014
23
and two eyebrow regions (for both the left and right eye); an upper, middle, lower left,
and lower right regions of the nose; and the left, middle, and right regions of the mouth.
These regions are normalized to attenuate variation in illumination. After that, the PZM is
employed in each subregion to extract a feature vector per region.
To find the pair of a twin, a set of feature vectors obtained from regions of input image will
be compared with the sets of feature vectors which are produced from the image in the
database. In the other words, the distance between the feature vector of a specific region in
input image and the feature vector of same region in an image of database (this action is
utilized for all regions). If there are 13 regions, then there are 13 distance values. The
average value of all distance values is calculated as the distance value between these two
images. Finally, the image from database with minimum distance value is considered as the
pair of input image. In the next Section, we discuss on the evaluation of the proposed method.
4. EXPERIMENTAL RESULTS
To evaluate the proposed method, two datasets is used: Twins Days Festival [2] and
Iranian Twin Society [1] with 520 and 600 pairs of identical twins images, respectively. Both
of The datasets includes facial image with different scales, rotation and different
illuminations. In Figure 1, subimages of some twin test images are shown.
The results of identical twins recognition of LRPZM is compared with PZM, ZM [20] and
LM [13]. Experiments results have been carried out in three steps according to order of
moment: (1) order n is in interval [1,6]; (2) order n is in interval [6,8]; and (3), order n is in
interval [9,10]. In this paper, N is set 10 (N=10) and j varies from 1 to 9. The error rate on the
classification of all geometric moments (LM, ZM, PZM and LRPZM) is calculated using
(15) and the results will be reported in Table 3.
Table 1. Feature vector elements based on the PZM
j value
FV j feature elements ( PZM km
) Number of
feature elementK M
4
4 0,1,2,3,4
56
5 0,1,2,3,4,5
6 0,1,2,3,4,5,6
7 0,1,2,3,4,5,6,7
8 0,1,2,3,4,5,6,7,8
9 0,1,2,3,4,5,6,7,8,9
10 0,1,2,3,4,5,6,7,8,9,10
6
6 0,1,2,3,4,5,6
45
7 0,1,2,3,4,5,6,7
8 0,1,2,3,4,5,6,7,8
9 0,1,2,3,4,5,6,7,8,9
10 0,1,2,3,4,5,6,7,8,9,10
9
9 0,1,2,3,4,5,6,7,8,9
21
10 0,1,2,3,4,5,6,7,8,9,10
6. Advanced Computational Intelligence: An International Journal (ACII),Vol.1, No.1, July 2014
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Table 2. Feature vector elements produced by geometric moments in each experiment.
Cat. LM feature elements ZM feature elements PZM feature elements
1
n=1, m=1
n=2, m=0,2
n=3, m=1,3
n=4, m=0,2,4
n=5, m=1,3,5
n=6, m=0,2,4,6
n=1, m=1
n=2, m=0,2
n=3, m=1,3
n=4, m=0,2,4
n=5, m=1,3,5
n=6, m=0,2,4,6
n=1, m=0,1
n=2, m=0,1,2
n=3, m=0,1,2,3 n=4,
m=0,1,2,3,4 n=5,
m=0,1,2,3,4,5 n=6,
m=0,1,2,3,4,5,6
2
n=6, m=0,2,4,6
n=7, m=1,3,5,7
n=8, m=0,2,4,6,8
n=6, m=0,2,4,6
n=7, m=1,3,5,7
n=8, m=0,2,4,6,8
n=6, m=0,1,2,3,4,5,6
n=7, m=0,1,2,3,4,5,6,7
n=8, m=0,1,2,3,4,5,6,7,8
3
n=9, m=1,3,5,7,9
n=10, m=0,2,4,6,8,10
n=9, m=1,3,5,7,9
n=10, m=,0,2,4,6,8,10
n=9, m=0,1,2,3,4,5,6,7,8,9
n=10, m=,0,1,2,3,4,5,6,7,8,9,10
Table 3 shows misclassification rates of LM, ZM, PZM and LRPZM. According to the table,
higher order moments of the PZM and LRPZM extracts more information for face recognition
while low-order moments have no significant effect on the system error. Also, the table shows
high misclassification rate of LM because the LM is sensitive to the rotation of input face.
Tables 3 shows the superiority of PZM to ZM because PZM is shift, rotation and scale
invariant while the ZM is shift and scale invariant. Also the size of feature vector created by
PZM is more than the one extracted by ZM and so, the global information obtained by PZM
is sufficient for identical twins recognition. As the both of the PZM and LRPZM have the
same feature vector length, the LRPZM takes the first place in Table 3 because the LRPZM
analyse the image in small region with more detail. In Figure 2, visual results of LRPZM on
pair of identical twins of two mentioned datasets are demonstrated. In this table, the first
row shows the results of LRPZM on the Twins Days Festival dataset [2] and second row
illustrates the results of LRPZM on the Iranian Twin Society dataset [1].
Table 3. Misclassification rate of each geometric moment in different categories.
Following parameters are means as No (Number), FE (Feature Elements) and ER
(Error Rate).
7. Advanced Computational Intelligence: An International Journal (ACII),Vol.1, No.1, July 2014
25
Figure 1. Drawing ellipse around the facial area of input image.
Figure 2. Some of testing identical twins which were correctly classified by LRPZM. The first row
refers to the results on the Twins Days Festival dataset [2] and the second row is the results on the
Iranian Twin Society dataset [1].
The experimental results prove the ability of LRPZM in extraction of informative feature vector
inside the subimages of a pair of identical twins in scaling, rotation and different illumination.
5. CONCLUSIONS
In this paper, a system presented to improve the recognition of a pair of identical twins. The
proposed method is based on the Local Region Pseudo-Zernike Moment (LRPZM) as a feature
extractor to recognize a pair of identical twins. For the face detection, AdaBoost method is
applied on the facial image of twins. After that, the obtained subimage is divided into some
local regions and then the PZM is utilized to construct feature vector elements for each region.
According to the experimental results, the proposed LRPZM system is able to extract
informative feature vector from input image and also is robust to rotation and scaling and
changing illumination.
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