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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohammed Talal Ghazal
Northern Technical University
Computer Science & IT Electrical & Electronics Engineering

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Face recognition based on curvelets, invariant moments features and SVM Mohammed Talal Ghazal; Karam Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14106

Abstract

Recent studies highlighted on face recognition methods. In this paper, a new algorithm is proposed for face recognition by combining Fast Discrete Curvelet Transform (FDCvT) and Invariant Moments with Support vector machine (SVM), which improves rate of face recognition in various situations. The reason of using this approach depends on two things. first, Curvelet transform which is a multi-resolution method, that can efficiently represent image edge discontinuities; Second, the Invariant Moments analysis which is a statistical method that meets with the translation, rotation and scale invariance in the image. Furthermore, SVM is employed to classify the face image based on the extracted features. This process is applied on each of ORL and Yale databases to evaluate the performance of the suggested method. Experimentally, the proposed method results show that our system can compose efficient and reasonable face recognition feature, and obtain useful recognition accuracy, which is able to face and side-face states detection of persons to decrease fault rate of production.
Pedestrian age estimation based on deep learning Nawal Younis Abdullah; Mohammed Talal Ghazal; Najwan Waisi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1548-1555

Abstract

The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular deep convolutional neural networks (DCNNs) pre-trained models have been used in this work, which have recently achieved the best performance in facial features extraction tasks: VGG-Face and ResNet-50. We combined these two models to increase the efficiency of the proposed system. We ran our experiments to evaluate the system based on the VGGFace2 dataset consisting of 3.31 million face images. From the experimental results, we observed a gap in the detection performances between those age groups: children from (00-10) years and elderly with 55 years and more. Moreover, it noted that the proposed pedestrian age estimation model performance is high, also a good result can be obtained by using the model for new purpose.
Driver drowsiness monitoring system based on facial Landmark detection with convolutional neural network for prediction Roaa Albasrawi; Fajer F. Fadhil; Mohammed Talal Ghazal
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3966

Abstract

Several factors often contribute to car accidents, most of them caused by human error, and the most notable are drowsiness, fatigue, distracted driving, and alcohol. Although self-driving cars are the best solution to save human lives and avoid car accidents, they are expensive. The roads in many countries are not prepared for the movement of this type of car. Scare new technologies included in modern cars, such as backup cameras and sensors, contributed to keeping drivers safer in this paper. A driver monitoring system is based on determining the driver’s face’s main points, which provide the required vital information for face analysis. The EfficientNet convolutional neural network (ConvNet) model is used for facial landmarks prediction, which is employed to detect face drowsiness and fatigue in real-time. The system is trained to detect multiple traits, including facial expressions, yawning and head poses. The results show that employing facial landmarks will assist in efficiently producing eyes and mouth features, which can assist in appropriately creating models to analyze drowsiness. Due to this, the proposed safety features are applicable and available in future vehicles.
Discover human poses similarity and action recognition based on machine learning Mohammed Moath Abdulghani; Mohammed Talal Ghazal; Anmar Burhan M. Salih
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4930

Abstract

In the computer vision field, human action recognition depending on pose estimation recently made considerable progress, especially by using deep learning, which improves recognition performance. Therefore, it has been employed in various applications, including sports and physical activity follow-up. This paper presents a technique for recognizing the human posture in different images and matching their pose similarity. This aims to evaluate the viability of employing computer vision techniques to verify a person's body pose during exercise and determine whether the pose is executed properly. Exercise is one strategy we use to maintain our health throughout life. Gymnastics and yoga are two examples of this type of exercise. The proposed algorithm identifies human action by recognizing the body's key points. The OpenPose library has been used to detect 18 key points of the human body. The action classification task is performed using the support vector machine (SVM) algorithm. Then, the algorithm computes the similarity of the human pose by comparing a model image to a test image to determine the matching score. Evaluations show that our method can perform at a competitive or state-of-the-art performance on a number of body pose datasets.
Co-Authors Anmar Burhan M. Salih Fajer F. Fadhil Karam Abdullah Mohammed Moath Abdulghani Najwan Waisi Nawal Younis Abdullah Roaa Albasrawi