Rabab Ramadan - Academia.edu (original) (raw)
Papers by Rabab Ramadan
International Journal of Advanced Computer Science and Applications, 2012
In this research an innovative fully automated 3D face compression and recognition system is pres... more In this research an innovative fully automated 3D face compression and recognition system is presented. Several novelties are introduced to make the system performance robust and efficient. These novelties include: First, an automatic pose correction and normalization process by using curvature analysis for nose tip detection and iterative closest point (ICP) image registration. Second, the use of spherical based wavelet coefficients for efficient representation of the 3D face. The spherical wavelet transformation is used to decompose the face image into multi-resolution sub images characterizing the underlying functions in a local fashion in both spacial and frequency domains. Two representation features based on spherical wavelet parameterization of the face image were proposed for the 3D face compression and recognition. Principle component analysis (PCA) is used to project to a low resolution sub-band. To evaluate the performance of the proposed approach, experiments were performed on the GAVAB face database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the face geometry image was found to generate good recognition results that outperform other methods working on the GAVAB database.
World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, May 24, 2008
International Journal of Electrical and Computer Engineering, Aug 29, 2008
Feature selection (FS) is a global optimization problem in machine learning that reduces the numb... more Feature selection (FS) is a global optimization problem in machine learning that reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). The algorithm
In this paper we propose an effective, low computational cost technique to find the orientation o... more In this paper we propose an effective, low computational cost technique to find the orientation of shapes that have several nonequally separated axes of symmetry. In our technique we define a simple method to calculate the average angle of the shape's axes of symmetry. The axes of symmetry of the shape could be detected using any of the well-known techniques reported in the literature. In the proposed technique we use the edge points of the shape to have the ability to deal with natural pictures like coins. The internal edges are used in addition to the external boundary edges to increase the orientation detection capabilities of the algorithm. First, the edge map of the image is extracted by applying Canny edge detector. Second, the center of the object is detected by calculating the average of the vertical and horizontal coordinates of the points of the edge map. Third, the total perpendicular absolute distances from the edge map points to the line that passes through the center point with specified angle are calculated. These calculations are repeated with different angles to find the angles of the minimum peaks of the calculated distances. Finally, if the shape has more than one minimum peak we use our averaging method to get the dominant direction angle of the shape or the shape orientation. By using this technique we only use the first moment of inertia and do not have to use any higher orders to reduce the computational cost.
The recognition of human faces, especially those with different orientations is a challenging and... more The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective schema for rotation invariant face recognition using log-polar transform and discrete cosine transform combined features. The rotation invariant feature extraction for a given face image involves applying the log-polar transform to eliminate the rotation
In this paper a novel rotation-invariant neural-based pattern recognition system is proposed. The... more In this paper a novel rotation-invariant neural-based pattern recognition system is proposed. The system incorporates a new image preprocessing technique to extract rotationinvariant descriptive patterns from the shapes. The proposed system applies a three phase algorithm on the shape image to extract the rotation-invariant pattern. First, the orientation angle of the shape is calculated using a newly developed shape orientation technique. The technique is effective, computationally inexpensive and can be applied to shapes with several nonequally separated axes of symmetry. A simple method to calculate the average angle of the shape's axes of symmetry is defined. In this technique, only the first moment of inertia is considered to reduce the computational cost. In the second phase, the image is rotated using a simple rotation technique to adapt its orientation angle to any specific reference angle. Finally in the third phase, the image preprocessor creates a symmetrical pattern about the axis with the calculated orientation angle and the perpendicular axis on it. Performing this operation in both the neural network training and application phases, ensures that the test rotated patterns will enter the network in the same position as in the training. Three different approaches were used to create the symmetrical patterns from the shapes. Experimental results indicate that the proposed approach is very effective and provide a recognition rate up to 99.5%.
The recognition of human faces, especially those with different orientations is a challenging and... more The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the logpolar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the pr...
2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009
Feature selection (FS) is a global optimization problem in machine learning that reduces the numb... more Feature selection (FS) is a global optimization problem in machine learning that reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). The algorithm
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010
In this paper we propose an effective, low computational cost technique to find the orientation o... more In this paper we propose an effective, low computational cost technique to find the orientation of shapes that have several nonequally separated axes of symmetry. In our technique we define a simple method to calculate the average angle of the shape's axes of symmetry. The axes of symmetry of the shape could be detected using any of the well-known techniques reported in the literature. In the proposed technique we use the edge points of the shape to have the ability to deal with natural pictures like coins. The internal edges are used in addition to the external boundary edges to increase the orientation detection capabilities of the algorithm. First, the edge map of the image is extracted by applying Canny edge detector. Second, the center of the object is detected by calculating the average of the vertical and horizontal coordinates of the points of the edge map. Third, the total perpendicular absolute distances from the edge map points to the line that passes through the center point with specified angle are calculated. These calculations are repeated with different angles to find the angles of the minimum peaks of the calculated distances. Finally, if the shape has more than one minimum peak we use our averaging method to get the dominant direction angle of the shape or the shape orientation. By using this technique we only use the first moment of inertia and do not have to use any higher orders to reduce the computational cost.
2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009
The recognition of human faces, especially those with different orientations is a challenging and... more The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective schema for rotation invariant face recognition using log-polar transform and discrete cosine transform combined features. The rotation invariant feature extraction for a given face image involves applying the log-polar transform to eliminate the rotation
With extensive application, the performance of unimodal biometrics systems has to face a diversit... more With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are nonhomogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swa...
Feature selection (FS) is a global optimization problem in machine learning, which reduces the nu... more Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). PSO is
International Journal of Advanced Research in Artificial Intelligence, 2015
this research is considered to be a research to attract attention to the 3D iris compression to s... more this research is considered to be a research to attract attention to the 3D iris compression to store the database of the iris. Actually, the 3D iris database cannot be found and in trying to solve this problem 2D iris database images are converted to 3D images just to implement the compression techniques used in 3D domain to test it and give an approximation results or to focus on this new direction in research. In this research a fully automated 3D iris compression and recognition system is presented. We use spherical based wavelet coefficients for efficient representation of the 3D iris. The spherical wavelet transformation is used to decompose the iris image into multi-resolution sub images. The representation of features based on spherical wavelet parameterization of the iris image was proposed for the 3D iris compression system. To evaluate the performance of the proposed approach, experiments were performed on the CASIA Iris database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the iris image was found to generate good recognition results.
Signal, Image and Video Processing, 2014
In this paper, a new method to compute the orientability of different shapes is defined. The prop... more In this paper, a new method to compute the orientability of different shapes is defined. The proposed technique is a boundary-geometry-based method that tends to take advantage of the simplicity of finding the orientability of an ellipse to obtain the orientability of any arbitrary shape. This is accomplished by finding the best-fitting ellipse of the shape. Initially, Canny edge detector is applied to obtain the edge map of the image. Convex hull points are identified and used to represent the shape. Three different approaches are presented to find the best-fitting ellipse. The three approaches use different definitions to the notion of the best-fitting ellipse of the shape. The first approach tries to find the minimum area ellipse that completely encloses the shape. While the second approach hardens the search constraints by searching for the minimum area ellipse whose center coincides with the center of the shape and completely encloses it. Alternatively, the third approach aims to find the maximum area ellipse that could be completely enclosed inside the shape and has the same center as of the shape. The three approaches utilize the particle swarm optimization technique with penalty function
International Journal of Advanced Computer Science and Applications, 2012
In this research an innovative fully automated 3D face compression and recognition system is pres... more In this research an innovative fully automated 3D face compression and recognition system is presented. Several novelties are introduced to make the system performance robust and efficient. These novelties include: First, an automatic pose correction and normalization process by using curvature analysis for nose tip detection and iterative closest point (ICP) image registration. Second, the use of spherical based wavelet coefficients for efficient representation of the 3D face. The spherical wavelet transformation is used to decompose the face image into multi-resolution sub images characterizing the underlying functions in a local fashion in both spacial and frequency domains. Two representation features based on spherical wavelet parameterization of the face image were proposed for the 3D face compression and recognition. Principle component analysis (PCA) is used to project to a low resolution sub-band. To evaluate the performance of the proposed approach, experiments were performed on the GAVAB face database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the face geometry image was found to generate good recognition results that outperform other methods working on the GAVAB database.
Unlike the best effort service provided by the internet today, next-generation wireless networks ... more Unlike the best effort service provided by the internet today, next-generation wireless networks will support real-time applications. This paper proposes an adaptive early packet discard (AEPD) policy to improve the performance of the real time TCP traffic over ATM networks and avoid the fragmentation problem. Three main aspects are incorporated in the proposed policy. First, providing quality-of-service (QoS) guaranteed for real-time applications by implementing a priority scheduling. Second, resolving the partially corrupted packets problem by differentiating the buffered cells of one packet from another. Third, adapting a threshold dynamically using Fuzzy logic based on the traffic behavior to maintain a high throughput under a variety of load conditions. The simulation is run for two priority classes of the input traffic: real time and non-real time classes. Simulation results show that the proposed AEPD policy improves throughput and fairness over that using static threshold under the same traffic conditions.
International Journal of Advanced Computer Science and Applications, 2012
In this research an innovative fully automated 3D face compression and recognition system is pres... more In this research an innovative fully automated 3D face compression and recognition system is presented. Several novelties are introduced to make the system performance robust and efficient. These novelties include: First, an automatic pose correction and normalization process by using curvature analysis for nose tip detection and iterative closest point (ICP) image registration. Second, the use of spherical based wavelet coefficients for efficient representation of the 3D face. The spherical wavelet transformation is used to decompose the face image into multi-resolution sub images characterizing the underlying functions in a local fashion in both spacial and frequency domains. Two representation features based on spherical wavelet parameterization of the face image were proposed for the 3D face compression and recognition. Principle component analysis (PCA) is used to project to a low resolution sub-band. To evaluate the performance of the proposed approach, experiments were performed on the GAVAB face database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the face geometry image was found to generate good recognition results that outperform other methods working on the GAVAB database.
World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, May 24, 2008
International Journal of Electrical and Computer Engineering, Aug 29, 2008
Feature selection (FS) is a global optimization problem in machine learning that reduces the numb... more Feature selection (FS) is a global optimization problem in machine learning that reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). The algorithm
In this paper we propose an effective, low computational cost technique to find the orientation o... more In this paper we propose an effective, low computational cost technique to find the orientation of shapes that have several nonequally separated axes of symmetry. In our technique we define a simple method to calculate the average angle of the shape's axes of symmetry. The axes of symmetry of the shape could be detected using any of the well-known techniques reported in the literature. In the proposed technique we use the edge points of the shape to have the ability to deal with natural pictures like coins. The internal edges are used in addition to the external boundary edges to increase the orientation detection capabilities of the algorithm. First, the edge map of the image is extracted by applying Canny edge detector. Second, the center of the object is detected by calculating the average of the vertical and horizontal coordinates of the points of the edge map. Third, the total perpendicular absolute distances from the edge map points to the line that passes through the center point with specified angle are calculated. These calculations are repeated with different angles to find the angles of the minimum peaks of the calculated distances. Finally, if the shape has more than one minimum peak we use our averaging method to get the dominant direction angle of the shape or the shape orientation. By using this technique we only use the first moment of inertia and do not have to use any higher orders to reduce the computational cost.
The recognition of human faces, especially those with different orientations is a challenging and... more The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective schema for rotation invariant face recognition using log-polar transform and discrete cosine transform combined features. The rotation invariant feature extraction for a given face image involves applying the log-polar transform to eliminate the rotation
In this paper a novel rotation-invariant neural-based pattern recognition system is proposed. The... more In this paper a novel rotation-invariant neural-based pattern recognition system is proposed. The system incorporates a new image preprocessing technique to extract rotationinvariant descriptive patterns from the shapes. The proposed system applies a three phase algorithm on the shape image to extract the rotation-invariant pattern. First, the orientation angle of the shape is calculated using a newly developed shape orientation technique. The technique is effective, computationally inexpensive and can be applied to shapes with several nonequally separated axes of symmetry. A simple method to calculate the average angle of the shape's axes of symmetry is defined. In this technique, only the first moment of inertia is considered to reduce the computational cost. In the second phase, the image is rotated using a simple rotation technique to adapt its orientation angle to any specific reference angle. Finally in the third phase, the image preprocessor creates a symmetrical pattern about the axis with the calculated orientation angle and the perpendicular axis on it. Performing this operation in both the neural network training and application phases, ensures that the test rotated patterns will enter the network in the same position as in the training. Three different approaches were used to create the symmetrical patterns from the shapes. Experimental results indicate that the proposed approach is very effective and provide a recognition rate up to 99.5%.
The recognition of human faces, especially those with different orientations is a challenging and... more The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the logpolar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the pr...
2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009
Feature selection (FS) is a global optimization problem in machine learning that reduces the numb... more Feature selection (FS) is a global optimization problem in machine learning that reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). The algorithm
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010
In this paper we propose an effective, low computational cost technique to find the orientation o... more In this paper we propose an effective, low computational cost technique to find the orientation of shapes that have several nonequally separated axes of symmetry. In our technique we define a simple method to calculate the average angle of the shape's axes of symmetry. The axes of symmetry of the shape could be detected using any of the well-known techniques reported in the literature. In the proposed technique we use the edge points of the shape to have the ability to deal with natural pictures like coins. The internal edges are used in addition to the external boundary edges to increase the orientation detection capabilities of the algorithm. First, the edge map of the image is extracted by applying Canny edge detector. Second, the center of the object is detected by calculating the average of the vertical and horizontal coordinates of the points of the edge map. Third, the total perpendicular absolute distances from the edge map points to the line that passes through the center point with specified angle are calculated. These calculations are repeated with different angles to find the angles of the minimum peaks of the calculated distances. Finally, if the shape has more than one minimum peak we use our averaging method to get the dominant direction angle of the shape or the shape orientation. By using this technique we only use the first moment of inertia and do not have to use any higher orders to reduce the computational cost.
2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009
The recognition of human faces, especially those with different orientations is a challenging and... more The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective schema for rotation invariant face recognition using log-polar transform and discrete cosine transform combined features. The rotation invariant feature extraction for a given face image involves applying the log-polar transform to eliminate the rotation
With extensive application, the performance of unimodal biometrics systems has to face a diversit... more With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are nonhomogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swa...
Feature selection (FS) is a global optimization problem in machine learning, which reduces the nu... more Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). PSO is
International Journal of Advanced Research in Artificial Intelligence, 2015
this research is considered to be a research to attract attention to the 3D iris compression to s... more this research is considered to be a research to attract attention to the 3D iris compression to store the database of the iris. Actually, the 3D iris database cannot be found and in trying to solve this problem 2D iris database images are converted to 3D images just to implement the compression techniques used in 3D domain to test it and give an approximation results or to focus on this new direction in research. In this research a fully automated 3D iris compression and recognition system is presented. We use spherical based wavelet coefficients for efficient representation of the 3D iris. The spherical wavelet transformation is used to decompose the iris image into multi-resolution sub images. The representation of features based on spherical wavelet parameterization of the iris image was proposed for the 3D iris compression system. To evaluate the performance of the proposed approach, experiments were performed on the CASIA Iris database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the iris image was found to generate good recognition results.
Signal, Image and Video Processing, 2014
In this paper, a new method to compute the orientability of different shapes is defined. The prop... more In this paper, a new method to compute the orientability of different shapes is defined. The proposed technique is a boundary-geometry-based method that tends to take advantage of the simplicity of finding the orientability of an ellipse to obtain the orientability of any arbitrary shape. This is accomplished by finding the best-fitting ellipse of the shape. Initially, Canny edge detector is applied to obtain the edge map of the image. Convex hull points are identified and used to represent the shape. Three different approaches are presented to find the best-fitting ellipse. The three approaches use different definitions to the notion of the best-fitting ellipse of the shape. The first approach tries to find the minimum area ellipse that completely encloses the shape. While the second approach hardens the search constraints by searching for the minimum area ellipse whose center coincides with the center of the shape and completely encloses it. Alternatively, the third approach aims to find the maximum area ellipse that could be completely enclosed inside the shape and has the same center as of the shape. The three approaches utilize the particle swarm optimization technique with penalty function
International Journal of Advanced Computer Science and Applications, 2012
In this research an innovative fully automated 3D face compression and recognition system is pres... more In this research an innovative fully automated 3D face compression and recognition system is presented. Several novelties are introduced to make the system performance robust and efficient. These novelties include: First, an automatic pose correction and normalization process by using curvature analysis for nose tip detection and iterative closest point (ICP) image registration. Second, the use of spherical based wavelet coefficients for efficient representation of the 3D face. The spherical wavelet transformation is used to decompose the face image into multi-resolution sub images characterizing the underlying functions in a local fashion in both spacial and frequency domains. Two representation features based on spherical wavelet parameterization of the face image were proposed for the 3D face compression and recognition. Principle component analysis (PCA) is used to project to a low resolution sub-band. To evaluate the performance of the proposed approach, experiments were performed on the GAVAB face database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the face geometry image was found to generate good recognition results that outperform other methods working on the GAVAB database.
Unlike the best effort service provided by the internet today, next-generation wireless networks ... more Unlike the best effort service provided by the internet today, next-generation wireless networks will support real-time applications. This paper proposes an adaptive early packet discard (AEPD) policy to improve the performance of the real time TCP traffic over ATM networks and avoid the fragmentation problem. Three main aspects are incorporated in the proposed policy. First, providing quality-of-service (QoS) guaranteed for real-time applications by implementing a priority scheduling. Second, resolving the partially corrupted packets problem by differentiating the buffered cells of one packet from another. Third, adapting a threshold dynamically using Fuzzy logic based on the traffic behavior to maintain a high throughput under a variety of load conditions. The simulation is run for two priority classes of the input traffic: real time and non-real time classes. Simulation results show that the proposed AEPD policy improves throughput and fairness over that using static threshold under the same traffic conditions.