Batch Normalization (BN) is an important preprocessing step to many deep learning applications. S... more Batch Normalization (BN) is an important preprocessing step to many deep learning applications. Since it is a data-dependent process, for some homogeneous datasets it is a redundant or even a performance-degrading process. In this paper, we propose an early-stage feasibility assessment method for estimating the benefits of applying BN on the given data batches. The proposed method uses a novel threshold-based approach to classify the training data batches into two sets according to their need for normalization. The need for normalization is decided based on the feature heterogeneity of the considered batch. The proposed approach is a pre-training processing, which implies no training overhead. The evaluation results show that the proposed approach achieves better performance mostly in small batch sizes than the traditional BN using MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets. Additionally, the network stability is increased by reducing the occurrence of internal variable transformation.
Breast cancer is the most common cancer in the world and the second most common type of cancer th... more Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on image processing. This approach is an ensemble model of two types of pre-trained vision transformer models, namely, Vision Transformer (ViT) and Data-Efficient Image Transformer (DeiT). The proposed ViT-DeiT model classifies breast cancer histopathology images into eight classes, four of which are categorized as benign, whereas the others are categorized as malignant. The BreakHis public dataset was used to evaluate the proposed model. The experimental results showed 98.17% accuracy, 98.18% precision, 98.08% recall, and a 98.12% F1 score.
Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges suc... more Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, large-number abnormal behavior, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormal behaviors. In this paper, our contribution is twofold. First, we introduce an annotated and labeled large-scale crowd abnormal behaviors Hajj dataset (HAJJv2). Second, we propose two methods of hybrid Convolutional Neural Networks (CNNs) and Random Forests (RFs) to detect and recognize Spatiotemporal abnormal behaviors in small and large-scales crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individuals detection method based on the magnitudes and orientations of Horn-Schunck optical flow is used to locate and track individuals with abnormal behaviors. A Kalman filter is employed in large-scale crowd videos to predict and track the detected individuals in the subsequent frames. Then, means, variances, and standard deviations statistical features are computed and fed to the RF to classify individuals with abnormal behaviors in the temporal domain. In large-scale crowds, we fine-tune the ResNet-50 model using YOLOv2 object detection technique to detect individuals with abnormal behaviors in the spatial domain. The proposed method achieves 99.77% and 93.71% of average Area Under the Curves (AUCs) on two public benchmark small-scale crowd datasets, UMN and UCSD, respectively. While the large-scale crowd method achieves 76.08% average AUC using the HAJJv2 dataset. Our method outperforms state-of-the-art methods using the small-scale crowd datasets with a margin of 1.67%, 6.06%, and 2.85% on UMN, UCSD Ped1, and UCSD Ped2, respectively. It also achieves a satisfactory result in the large-scale crowds. Keywords Abnormal behaviors • small-scale crowd • large-scale crowd • convolutional neural network • random forest • detection • tracking • recognition
The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwi... more The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwide, is highly dependent on the Saudi population embracing the concept of participating in genetic testing. However, genetic data sharing and artificial intelligence (AI) in genomics are critical public issues in medical care and scientific research. The present study was aimed to examine the awareness, knowledge, and attitude of the Saudi society towards the SHGP, the sharing and privacy of genetic data resulting from the SHGP, and the role of AI in genetic data analysis and regulations. Results of a questionnaire survey with 804 respondents revealed moderate awareness and attitude towards the SHGP and minimal knowledge regarding its benefits and applications. Respondents demonstrated a low level of knowledge regarding the privacy of genetic data. A generally positive attitude was found towards the outcomes of the SHGP and genetic data sharing for medical and scientific research. The hig...
Our LSLF dataset consists of 1,195,976 labeled face images for 11,459 individuals. These images a... more Our LSLF dataset consists of 1,195,976 labeled face images for 11,459 individuals. These images are stored in JPEG format with a total size of 5.36 GB. Individuals have a minimum of 1 face image and a maximum of 1,157 face images. The average number of face images per individual is 104. Each image is automatically named as (PersonName VideoNumber FrameNumber ImageNuumber) and stored in the related individual folder.
The nucleus and micronucleus images in this dataset are collected manually from Google images. Ma... more The nucleus and micronucleus images in this dataset are collected manually from Google images. Many of these images are in RGB color while a few of them are in grayscale. The dataset includes 148 nucleus images and 158 micronucleus images. The images are manually curated, cropped, and labeled into these two classes by a domain of experts in biology. The images have different sizes and different resolutions. The sizes and shapes for nucleuses and micronucleuses images differ from one image to another. Each image may contain one or more nucleus or micronucleus.
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017
Many biometrics and security systems use facial information to obtain an individual identificatio... more Many biometrics and security systems use facial information to obtain an individual identification and recognition. Classifying a race from a face image can provide a strong hint to search for facial identity and criminal identification. Current facial race classification methods are confined only to constrained non-partially occluded frontal faces. Challenges remain under unconstrained environments such as partial occlusions and pose variations. In this paper, we propose a Convolutional Neural Network (CNN) model to classify facial races with partial occlusions and pose variations. The proposed model is trained using a broad and balanced racial distributed face image dataset. The model is trained on four major human races, Caucasian, Indian, Mongolian, and Negroid. Our model is evaluated against the state-of-the-art methods on a constrained face test dataset. Also, an evaluation of the proposed model and human performance is conducted and compared on our new unconstrained facial race benchmark (CIMN) dataset. Our results show that our model achieves 95.1% of race classification accuracy on constrained frontal faces. Also, the proposed model achieves a comparable classification accuracy result compared to human performance with a margin of 6.2% under the current challenges in the unconstrained environment.
Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges suc... more Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, a large number of abnormal behaviors, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormalities. In this paper, our contribution is two-fold. First, we introduce an annotated and labeled large-scale crowd abnormal behavior Hajj dataset, HAJJv2. Second, we propose two methods of hybrid convolutional neural networks (CNNs) and random forests (RFs) to detect and recognize spatio-temporal abnormal behaviors in small and large-scale crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individual detection method based on the magnitudes and orientations of Horn–Schunck optical flow is proposed to locate and track individuals with abnorm...
2020 12th International Conference on Knowledge and Systems Engineering (KSE), 2020
Recognizing normality and abnormality from heart sound recordings (phonocardiograms or PCG) has p... more Recognizing normality and abnormality from heart sound recordings (phonocardiograms or PCG) has promote a scientific research in cardiology. However, only a small number of PCG recordings is publicly available. Also, current recognition approaches have not reached a satisfiable accuracy. In this paper, we apply transfer learning to automate the recognition for heart rates normalities and abnormalities. Mel Frequency Cepstrum Coefficients (MFCC) signal representation is adopted to transform the output feature into a PCG image which is used as an input to pre-trained Convolutional Neural Network (CNN) models. Then, several deep pre-trained CNN models are fine-tuned. The proposed approach is effective. Inception-ResNet-v2 pre-trained model achieves better performance than other models and other recent methods with an 89.5% of average classification accuracy using Pascal Heart Sound Challenge dataset.
Face detection methods have relied on face datasets for training. However, existing face datasets... more Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large number of unconstrained multi-view and partially occluded faces. The faces have many variations in color and grayscale, image quality, image resolution, image illumination, image background, image illusion, human face, cartoon face, facial expression, light and severe partial facial occlusion, make up, gender, age, and race. Many of these faces are partially occluded with accessories such as tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves, microphones, or other objects or persons. The LSLF dataset is currently the largest labeled face image dataset in the literature in terms of the number of lab...
2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), 2020
The study of crowd scenes has become an interesting research area. Crowd flow collision is a crit... more The study of crowd scenes has become an interesting research area. Crowd flow collision is a critical problem since it may cause injuries or death to people. Current research methods on crowd flow collisions are only confined in 1D and 2D straight line directions. In this paper, we propose a 3D simulation for crowd flow collisions in different directions within an entry and an exit at a gate. The simulation efficiency is decreased when a number of agents is increased. The simulation frames per second are dropped below 35 when the characters are more than 100.
2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), 2020
Nucleus is the main component in a human cell. Excellent nucleus shape observation methods help e... more Nucleus is the main component in a human cell. Excellent nucleus shape observation methods help endorse scientific research in medical and biological fields. Nucleus shape observation methods are performed manually by humans through medical labs. Abnormal nucleus (Micronucleus) is caused by drugs and other toxical factors. Current methods are only confined in detecting and segmenting nucleuses images from different human tissues. None of the these methods has tackled the problem of automating the nucleus and micronucleus recognition. In this paper, first, we apply a deep transfer learning to automate the recognition of nucleus and micronucleus from microscopy images. Second, we introduce our dataset which consists of manually collected nucleus and micronucleus image examples to train and evaluate the performance of the proposed method. The examples are manually curated by domain of experts in the biology. The nucleus and micronucleus images are used as an input to feed several pre-t...
Journal of Ambient Intelligence and Humanized Computing, 2021
Hajj is an annual Islamic pilgrimage, which is attended by millions of pilgrims every year. There... more Hajj is an annual Islamic pilgrimage, which is attended by millions of pilgrims every year. Therefore, there are many security management problems. The existing solutions can only solve the problems of a small-scale crowd, which contains a simple abnormal behavior and a clear surveillance video. However, the performance hasn’t reached a satisfactory result for a large-scale crowd. Therefore, we propose an abnormal behavior detection method based on optical flow and generative adversarial network (GAN). There are three main contributions in this paper. Firstly, the dynamic features of the model are extracted based on the optical flows. The effectiveness of the features is validated by experiments. Secondly, we propose an optical flow framework based on GAN and use a transfer learning strategy to detect behavioral abnormalities in large-scale crowd scenes. The framework uses U-Net and Flownet to generate and distinguish the normal and abnormal behaviors of individuals within the massi...
Face detection in unconstrained environments is a challenging problem due to partial occlusions w... more Face detection in unconstrained environments is a challenging problem due to partial occlusions with pose variations. Existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. In this paper, our contributions are two-fold. First, we propose our Large-Scale Deep Learning (LSDL), a method that requires a single Convolutional Neural Network (CNN) model without computing any hand-crafted features to detect faces. The model is trained with a large number of face training examples that cover most partial occlusions and non-partial occlusions facial appearances to detect unconstrained multi-view partially occluded and non-partially occluded faces. The LSDL face detection method is achieved by selecting detection windows with the highest confidence scores using a threshold. Second, we introduce new four image datasets consisting of large-scale labeled face dataset, noisy large-scale labeled non-face dataset, CrowdFaces datase...
The study of crowd scenes has become an interesting research area. Crowd flow collision is a crit... more The study of crowd scenes has become an interesting research area. Crowd flow collision is a critical problem since it may cause injuries or death to people. Current research methods on crowd flow collisions are only confined in 1D and 2D straight line directions. In this paper, we propose a 3D simulation for crowd flow collisions in different directions within an entry and an exit at a gate. The simulation efficiency is decreased when a number of agents is increased. The simulation frames per second are dropped below 35 when the characters are more than 100.
Batch Normalization (BN) is an important preprocessing step to many deep learning applications. S... more Batch Normalization (BN) is an important preprocessing step to many deep learning applications. Since it is a data-dependent process, for some homogeneous datasets it is a redundant or even a performance-degrading process. In this paper, we propose an early-stage feasibility assessment method for estimating the benefits of applying BN on the given data batches. The proposed method uses a novel threshold-based approach to classify the training data batches into two sets according to their need for normalization. The need for normalization is decided based on the feature heterogeneity of the considered batch. The proposed approach is a pre-training processing, which implies no training overhead. The evaluation results show that the proposed approach achieves better performance mostly in small batch sizes than the traditional BN using MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets. Additionally, the network stability is increased by reducing the occurrence of internal variable transformation.
Breast cancer is the most common cancer in the world and the second most common type of cancer th... more Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on image processing. This approach is an ensemble model of two types of pre-trained vision transformer models, namely, Vision Transformer (ViT) and Data-Efficient Image Transformer (DeiT). The proposed ViT-DeiT model classifies breast cancer histopathology images into eight classes, four of which are categorized as benign, whereas the others are categorized as malignant. The BreakHis public dataset was used to evaluate the proposed model. The experimental results showed 98.17% accuracy, 98.18% precision, 98.08% recall, and a 98.12% F1 score.
Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges suc... more Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, large-number abnormal behavior, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormal behaviors. In this paper, our contribution is twofold. First, we introduce an annotated and labeled large-scale crowd abnormal behaviors Hajj dataset (HAJJv2). Second, we propose two methods of hybrid Convolutional Neural Networks (CNNs) and Random Forests (RFs) to detect and recognize Spatiotemporal abnormal behaviors in small and large-scales crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individuals detection method based on the magnitudes and orientations of Horn-Schunck optical flow is used to locate and track individuals with abnormal behaviors. A Kalman filter is employed in large-scale crowd videos to predict and track the detected individuals in the subsequent frames. Then, means, variances, and standard deviations statistical features are computed and fed to the RF to classify individuals with abnormal behaviors in the temporal domain. In large-scale crowds, we fine-tune the ResNet-50 model using YOLOv2 object detection technique to detect individuals with abnormal behaviors in the spatial domain. The proposed method achieves 99.77% and 93.71% of average Area Under the Curves (AUCs) on two public benchmark small-scale crowd datasets, UMN and UCSD, respectively. While the large-scale crowd method achieves 76.08% average AUC using the HAJJv2 dataset. Our method outperforms state-of-the-art methods using the small-scale crowd datasets with a margin of 1.67%, 6.06%, and 2.85% on UMN, UCSD Ped1, and UCSD Ped2, respectively. It also achieves a satisfactory result in the large-scale crowds. Keywords Abnormal behaviors • small-scale crowd • large-scale crowd • convolutional neural network • random forest • detection • tracking • recognition
The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwi... more The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwide, is highly dependent on the Saudi population embracing the concept of participating in genetic testing. However, genetic data sharing and artificial intelligence (AI) in genomics are critical public issues in medical care and scientific research. The present study was aimed to examine the awareness, knowledge, and attitude of the Saudi society towards the SHGP, the sharing and privacy of genetic data resulting from the SHGP, and the role of AI in genetic data analysis and regulations. Results of a questionnaire survey with 804 respondents revealed moderate awareness and attitude towards the SHGP and minimal knowledge regarding its benefits and applications. Respondents demonstrated a low level of knowledge regarding the privacy of genetic data. A generally positive attitude was found towards the outcomes of the SHGP and genetic data sharing for medical and scientific research. The hig...
Our LSLF dataset consists of 1,195,976 labeled face images for 11,459 individuals. These images a... more Our LSLF dataset consists of 1,195,976 labeled face images for 11,459 individuals. These images are stored in JPEG format with a total size of 5.36 GB. Individuals have a minimum of 1 face image and a maximum of 1,157 face images. The average number of face images per individual is 104. Each image is automatically named as (PersonName VideoNumber FrameNumber ImageNuumber) and stored in the related individual folder.
The nucleus and micronucleus images in this dataset are collected manually from Google images. Ma... more The nucleus and micronucleus images in this dataset are collected manually from Google images. Many of these images are in RGB color while a few of them are in grayscale. The dataset includes 148 nucleus images and 158 micronucleus images. The images are manually curated, cropped, and labeled into these two classes by a domain of experts in biology. The images have different sizes and different resolutions. The sizes and shapes for nucleuses and micronucleuses images differ from one image to another. Each image may contain one or more nucleus or micronucleus.
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017
Many biometrics and security systems use facial information to obtain an individual identificatio... more Many biometrics and security systems use facial information to obtain an individual identification and recognition. Classifying a race from a face image can provide a strong hint to search for facial identity and criminal identification. Current facial race classification methods are confined only to constrained non-partially occluded frontal faces. Challenges remain under unconstrained environments such as partial occlusions and pose variations. In this paper, we propose a Convolutional Neural Network (CNN) model to classify facial races with partial occlusions and pose variations. The proposed model is trained using a broad and balanced racial distributed face image dataset. The model is trained on four major human races, Caucasian, Indian, Mongolian, and Negroid. Our model is evaluated against the state-of-the-art methods on a constrained face test dataset. Also, an evaluation of the proposed model and human performance is conducted and compared on our new unconstrained facial race benchmark (CIMN) dataset. Our results show that our model achieves 95.1% of race classification accuracy on constrained frontal faces. Also, the proposed model achieves a comparable classification accuracy result compared to human performance with a margin of 6.2% under the current challenges in the unconstrained environment.
Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges suc... more Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, a large number of abnormal behaviors, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormalities. In this paper, our contribution is two-fold. First, we introduce an annotated and labeled large-scale crowd abnormal behavior Hajj dataset, HAJJv2. Second, we propose two methods of hybrid convolutional neural networks (CNNs) and random forests (RFs) to detect and recognize spatio-temporal abnormal behaviors in small and large-scale crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individual detection method based on the magnitudes and orientations of Horn–Schunck optical flow is proposed to locate and track individuals with abnorm...
2020 12th International Conference on Knowledge and Systems Engineering (KSE), 2020
Recognizing normality and abnormality from heart sound recordings (phonocardiograms or PCG) has p... more Recognizing normality and abnormality from heart sound recordings (phonocardiograms or PCG) has promote a scientific research in cardiology. However, only a small number of PCG recordings is publicly available. Also, current recognition approaches have not reached a satisfiable accuracy. In this paper, we apply transfer learning to automate the recognition for heart rates normalities and abnormalities. Mel Frequency Cepstrum Coefficients (MFCC) signal representation is adopted to transform the output feature into a PCG image which is used as an input to pre-trained Convolutional Neural Network (CNN) models. Then, several deep pre-trained CNN models are fine-tuned. The proposed approach is effective. Inception-ResNet-v2 pre-trained model achieves better performance than other models and other recent methods with an 89.5% of average classification accuracy using Pascal Heart Sound Challenge dataset.
Face detection methods have relied on face datasets for training. However, existing face datasets... more Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large number of unconstrained multi-view and partially occluded faces. The faces have many variations in color and grayscale, image quality, image resolution, image illumination, image background, image illusion, human face, cartoon face, facial expression, light and severe partial facial occlusion, make up, gender, age, and race. Many of these faces are partially occluded with accessories such as tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves, microphones, or other objects or persons. The LSLF dataset is currently the largest labeled face image dataset in the literature in terms of the number of lab...
2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), 2020
The study of crowd scenes has become an interesting research area. Crowd flow collision is a crit... more The study of crowd scenes has become an interesting research area. Crowd flow collision is a critical problem since it may cause injuries or death to people. Current research methods on crowd flow collisions are only confined in 1D and 2D straight line directions. In this paper, we propose a 3D simulation for crowd flow collisions in different directions within an entry and an exit at a gate. The simulation efficiency is decreased when a number of agents is increased. The simulation frames per second are dropped below 35 when the characters are more than 100.
2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), 2020
Nucleus is the main component in a human cell. Excellent nucleus shape observation methods help e... more Nucleus is the main component in a human cell. Excellent nucleus shape observation methods help endorse scientific research in medical and biological fields. Nucleus shape observation methods are performed manually by humans through medical labs. Abnormal nucleus (Micronucleus) is caused by drugs and other toxical factors. Current methods are only confined in detecting and segmenting nucleuses images from different human tissues. None of the these methods has tackled the problem of automating the nucleus and micronucleus recognition. In this paper, first, we apply a deep transfer learning to automate the recognition of nucleus and micronucleus from microscopy images. Second, we introduce our dataset which consists of manually collected nucleus and micronucleus image examples to train and evaluate the performance of the proposed method. The examples are manually curated by domain of experts in the biology. The nucleus and micronucleus images are used as an input to feed several pre-t...
Journal of Ambient Intelligence and Humanized Computing, 2021
Hajj is an annual Islamic pilgrimage, which is attended by millions of pilgrims every year. There... more Hajj is an annual Islamic pilgrimage, which is attended by millions of pilgrims every year. Therefore, there are many security management problems. The existing solutions can only solve the problems of a small-scale crowd, which contains a simple abnormal behavior and a clear surveillance video. However, the performance hasn’t reached a satisfactory result for a large-scale crowd. Therefore, we propose an abnormal behavior detection method based on optical flow and generative adversarial network (GAN). There are three main contributions in this paper. Firstly, the dynamic features of the model are extracted based on the optical flows. The effectiveness of the features is validated by experiments. Secondly, we propose an optical flow framework based on GAN and use a transfer learning strategy to detect behavioral abnormalities in large-scale crowd scenes. The framework uses U-Net and Flownet to generate and distinguish the normal and abnormal behaviors of individuals within the massi...
Face detection in unconstrained environments is a challenging problem due to partial occlusions w... more Face detection in unconstrained environments is a challenging problem due to partial occlusions with pose variations. Existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. In this paper, our contributions are two-fold. First, we propose our Large-Scale Deep Learning (LSDL), a method that requires a single Convolutional Neural Network (CNN) model without computing any hand-crafted features to detect faces. The model is trained with a large number of face training examples that cover most partial occlusions and non-partial occlusions facial appearances to detect unconstrained multi-view partially occluded and non-partially occluded faces. The LSDL face detection method is achieved by selecting detection windows with the highest confidence scores using a threshold. Second, we introduce new four image datasets consisting of large-scale labeled face dataset, noisy large-scale labeled non-face dataset, CrowdFaces datase...
The study of crowd scenes has become an interesting research area. Crowd flow collision is a crit... more The study of crowd scenes has become an interesting research area. Crowd flow collision is a critical problem since it may cause injuries or death to people. Current research methods on crowd flow collisions are only confined in 1D and 2D straight line directions. In this paper, we propose a 3D simulation for crowd flow collisions in different directions within an entry and an exit at a gate. The simulation efficiency is decreased when a number of agents is increased. The simulation frames per second are dropped below 35 when the characters are more than 100.
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Papers by Tarik Alafif