Samy Bakheet - Academia.edu (original) (raw)

Papers by Samy Bakheet

Research paper thumbnail of Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features

Applied Sciences

Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerpri... more Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified ...

Research paper thumbnail of A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

Applied Sciences, 2022

Amongst all biometric-based personal authentication systems, a fingerprint that gives each person... more Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresp...

Research paper thumbnail of Vision-based representation and recognition of human activities in image sequences

Research paper thumbnail of A Survey on Content-based Visual Information Retrieval

Images have always been seen as an effective medium for visual data presentation. In recent years... more Images have always been seen as an effective medium for visual data presentation. In recent years, a tremendous combination of images and videos have been grown up rapidly due to technology evolution. Content-Based Visual Information Retrieval (CBVIR), which is the process of searching for images via the end user's predefined specific pattern (hand sketch, camera capture, or web scrawled). CBVIR is still far away from achieving objective satisfaction due to image content-based search engines (for ex. Google image-based search) still not completely satisfying. This problem occurs because of the semantic gap between low and high visual level features representation of the image. In this paper, The state-ofart CBVIR techniques for multi-purpose applications are survived. The architecture of the promising CBVIR pipelines in recent decades, which witness the arising of computer vision is highlighted. Mathematical, machine, and deep learning-based CBVIR systems are introduced. Althoug...

Research paper thumbnail of Robust hand gesture recognition using multiple shape-oriented visual cues

Robust vision-based hand pose estimation is highly sought but still remains a challenging task, d... more Robust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classificatio...

Research paper thumbnail of A Deep Neural Approach for Real-Time Malignant Melanoma Detection

Applied Mathematics & Information Sciences

Research paper thumbnail of Adaptive Multimodal Feature Fusion for Content-Based Image Classification and Retrieval

Applied Mathematics & Information Sciences

Research paper thumbnail of Computer-Aided Diagnosis of Malignant Melanoma Using Gabor-Based Entropic Features and Multilevel Neural Networks

The American Cancer Society has recently stated that malignant melanoma is the most serious type ... more The American Cancer Society has recently stated that malignant melanoma is the most serious type of skin cancer, and it is almost 100% curable, if it is detected and treated early. In this paper, we present a fully automated neural framework for real-time melanoma detection, where a low-dimensional, computationally inexpensive but highly discriminative descriptor for skin lesions is derived from local patterns of Gabor-based entropic features. The input skin image is first preprocessed by filtering and histogram equalization to reduce noise and enhance image quality. An automatic thresholding by the optimized formula of Otsu’s method is used for segmenting out lesion regions from the surrounding healthy skin regions. Then, an extensive set of optimized Gabor-based features is computed to characterize segmented skin lesions. Finally, the normalized features are fed into a trained Multilevel Neural Network to classify each pigmented skin lesion in a given dermoscopic image as benign o...

Research paper thumbnail of A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

Brain Sciences

Due to their high distinctiveness, robustness to illumination and simple computation, Histogram o... more Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.

Research paper thumbnail of Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification

Computers in Biology and Medicine

Research paper thumbnail of Chord-Length Shape Features for License Plate Character Recognition

Journal of Russian Laser Research

Research paper thumbnail of An Elliptical Boundary Skin Model For Hand Detection Based on HSV Color Space

Information Sciences Letters

Research paper thumbnail of Recognition of Human Actions Based on Temporal Motion Templates

British Journal of Applied Science & Technology

Research paper thumbnail of An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Computation

Early detection of skin cancer through improved techniques and innovative technologies has the gr... more Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine) model on an optimized set of HOG (Histogram of Oriented Gradient) based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively), without sacrificing computational soundness.

Research paper thumbnail of A Fuzzy Framework for Real-Time Gesture Spotting and Recognition

Journal of Russian Laser Research

Research paper thumbnail of Hand Gesture Recognition Using Optimized Local Gabor Features

Journal of Computational and Theoretical Nanoscience

Research paper thumbnail of A Discriminative Framework for Action Recognition Using f-HOL Features

Information, 2016

Inspired by the overwhelming success of Histogram of Oriented Gradients (HOG) features in many vi... more Inspired by the overwhelming success of Histogram of Oriented Gradients (HOG) features in many vision tasks, in this paper, we present an innovative compact feature descriptor called fuzzy Histogram of Oriented Lines (f-HOL) for action recognition, which is a distinct variant of the HOG feature descriptor. The intuitive idea of these features is based on the observation that the slide area of the human body skeleton can be viewed as a spatiotemporal 3D surface, when observing a certain action being performed in a video. The f-HOL descriptor possesses an immense competitive advantage, not only of being quite robust to small geometric transformations where the small translation and rotations make no large fluctuations in histogram values, but also of not being very sensitive under varying illumination conditions. The extracted features are then fed into a discriminative conditional model based on Latent-Dynamic Conditional random fields (LDCRFs) to learn to recognize actions from video frames. When tested on the benchmark Weizmann dataset, the proposed framework substantially supersedes most existing state-of-the-art approaches, achieving an overall recognition rate of 98.2%. Furthermore, due to its low computational demands, the framework is properly amenable for integration into real-time applications.

Research paper thumbnail of A Hybrid Cascade Approach for Human Skin Segmentation

British Journal of Mathematics & Computer Science, 2016

Research paper thumbnail of Toward Robust Action Retrieval in Video

British Machine Vision Conference, 2010

Retrieving human actions from video databases is a paramount but challenging task in computer vis... more Retrieving human actions from video databases is a paramount but challenging task in computer vision. In this work, we develop such a framework for robustly recognizing human actions in video sequences. The contribution of the paper is twofold. First a reliable neural model, the Multi-level Sigmoidal Neural Network (MSNN) as a classifier for the task of action recognition is presented. Second we unfold how the temporal shape variations can be accurately captured based on both temporal self-similarities and fuzzy log-polar histograms. When the method is evaluated on the popular KTH dataset, an average recognition rate of 94.3% is obtained. Such results have the potential to compare very favorably to those of other investigators published in the literature. Further the approach is amenable for real-time applications due to its low computational requirements.

Research paper thumbnail of Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features

Applied Sciences

Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerpri... more Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified ...

Research paper thumbnail of A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

Applied Sciences, 2022

Amongst all biometric-based personal authentication systems, a fingerprint that gives each person... more Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresp...

Research paper thumbnail of Vision-based representation and recognition of human activities in image sequences

Research paper thumbnail of A Survey on Content-based Visual Information Retrieval

Images have always been seen as an effective medium for visual data presentation. In recent years... more Images have always been seen as an effective medium for visual data presentation. In recent years, a tremendous combination of images and videos have been grown up rapidly due to technology evolution. Content-Based Visual Information Retrieval (CBVIR), which is the process of searching for images via the end user's predefined specific pattern (hand sketch, camera capture, or web scrawled). CBVIR is still far away from achieving objective satisfaction due to image content-based search engines (for ex. Google image-based search) still not completely satisfying. This problem occurs because of the semantic gap between low and high visual level features representation of the image. In this paper, The state-ofart CBVIR techniques for multi-purpose applications are survived. The architecture of the promising CBVIR pipelines in recent decades, which witness the arising of computer vision is highlighted. Mathematical, machine, and deep learning-based CBVIR systems are introduced. Althoug...

Research paper thumbnail of Robust hand gesture recognition using multiple shape-oriented visual cues

Robust vision-based hand pose estimation is highly sought but still remains a challenging task, d... more Robust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classificatio...

Research paper thumbnail of A Deep Neural Approach for Real-Time Malignant Melanoma Detection

Applied Mathematics & Information Sciences

Research paper thumbnail of Adaptive Multimodal Feature Fusion for Content-Based Image Classification and Retrieval

Applied Mathematics & Information Sciences

Research paper thumbnail of Computer-Aided Diagnosis of Malignant Melanoma Using Gabor-Based Entropic Features and Multilevel Neural Networks

The American Cancer Society has recently stated that malignant melanoma is the most serious type ... more The American Cancer Society has recently stated that malignant melanoma is the most serious type of skin cancer, and it is almost 100% curable, if it is detected and treated early. In this paper, we present a fully automated neural framework for real-time melanoma detection, where a low-dimensional, computationally inexpensive but highly discriminative descriptor for skin lesions is derived from local patterns of Gabor-based entropic features. The input skin image is first preprocessed by filtering and histogram equalization to reduce noise and enhance image quality. An automatic thresholding by the optimized formula of Otsu’s method is used for segmenting out lesion regions from the surrounding healthy skin regions. Then, an extensive set of optimized Gabor-based features is computed to characterize segmented skin lesions. Finally, the normalized features are fed into a trained Multilevel Neural Network to classify each pigmented skin lesion in a given dermoscopic image as benign o...

Research paper thumbnail of A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

Brain Sciences

Due to their high distinctiveness, robustness to illumination and simple computation, Histogram o... more Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.

Research paper thumbnail of Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification

Computers in Biology and Medicine

Research paper thumbnail of Chord-Length Shape Features for License Plate Character Recognition

Journal of Russian Laser Research

Research paper thumbnail of An Elliptical Boundary Skin Model For Hand Detection Based on HSV Color Space

Information Sciences Letters

Research paper thumbnail of Recognition of Human Actions Based on Temporal Motion Templates

British Journal of Applied Science & Technology

Research paper thumbnail of An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Computation

Early detection of skin cancer through improved techniques and innovative technologies has the gr... more Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine) model on an optimized set of HOG (Histogram of Oriented Gradient) based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively), without sacrificing computational soundness.

Research paper thumbnail of A Fuzzy Framework for Real-Time Gesture Spotting and Recognition

Journal of Russian Laser Research

Research paper thumbnail of Hand Gesture Recognition Using Optimized Local Gabor Features

Journal of Computational and Theoretical Nanoscience

Research paper thumbnail of A Discriminative Framework for Action Recognition Using f-HOL Features

Information, 2016

Inspired by the overwhelming success of Histogram of Oriented Gradients (HOG) features in many vi... more Inspired by the overwhelming success of Histogram of Oriented Gradients (HOG) features in many vision tasks, in this paper, we present an innovative compact feature descriptor called fuzzy Histogram of Oriented Lines (f-HOL) for action recognition, which is a distinct variant of the HOG feature descriptor. The intuitive idea of these features is based on the observation that the slide area of the human body skeleton can be viewed as a spatiotemporal 3D surface, when observing a certain action being performed in a video. The f-HOL descriptor possesses an immense competitive advantage, not only of being quite robust to small geometric transformations where the small translation and rotations make no large fluctuations in histogram values, but also of not being very sensitive under varying illumination conditions. The extracted features are then fed into a discriminative conditional model based on Latent-Dynamic Conditional random fields (LDCRFs) to learn to recognize actions from video frames. When tested on the benchmark Weizmann dataset, the proposed framework substantially supersedes most existing state-of-the-art approaches, achieving an overall recognition rate of 98.2%. Furthermore, due to its low computational demands, the framework is properly amenable for integration into real-time applications.

Research paper thumbnail of A Hybrid Cascade Approach for Human Skin Segmentation

British Journal of Mathematics & Computer Science, 2016

Research paper thumbnail of Toward Robust Action Retrieval in Video

British Machine Vision Conference, 2010

Retrieving human actions from video databases is a paramount but challenging task in computer vis... more Retrieving human actions from video databases is a paramount but challenging task in computer vision. In this work, we develop such a framework for robustly recognizing human actions in video sequences. The contribution of the paper is twofold. First a reliable neural model, the Multi-level Sigmoidal Neural Network (MSNN) as a classifier for the task of action recognition is presented. Second we unfold how the temporal shape variations can be accurately captured based on both temporal self-similarities and fuzzy log-polar histograms. When the method is evaluated on the popular KTH dataset, an average recognition rate of 94.3% is obtained. Such results have the potential to compare very favorably to those of other investigators published in the literature. Further the approach is amenable for real-time applications due to its low computational requirements.