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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Ahmed Abdulsahib Hashim
University of Information Technology and Communications

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3D face creation via 2D images within blender virtual environment Ali Salim Rasheed; Rasool Hasan Finjan; Ahmed Abdulsahib Hashim; Mustafa Murtdha Al-Saeedi
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp457-464

Abstract

Animation and virtual reality movie-making technologies are still witnessing significant progress to this day. Building and stimulating virtual characters inside these applications is a goal. Build a 3D face via using some special tools inside the virtual world is the most important part of identifying a 3D animation. Keen Tools Face Builder add-on for Blender. Interested in creating a 3D face of a famous figure, artist or the general public by adopting several 2D images added to the virtual blinder software environment. The main problem facing these tools is that they deal with high-resolution and sharpness pictures because some images that contain blurring, the result is to build a 3D face model that contains design distortions and non- clearly. in this proposed paper, build a data set for 2D pictures of a specific character (actor), at a resolution of 1920 x 1080 pixels. These images were caught by the camera, different in sharpness and blurring (four types of blurry). Using the “Laplacian Filter algorithm” and OpenCV library with Python language, to isolate blurry from sharpness 2D images. Sharpness images used to build a 3D face model that gave real and similar results to the character in the pictures. 
Arabic handwritten digits recognition based on convolutional neural networks with resnet-34 model Rasool Hasan Finjan; Ali Salim Rasheed; Ahmed Abdulsahib Hashim; Mustafa Murtdha
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp174-178

Abstract

Handwritten digits recognition has attracted the attention of researchers in pattern recognition fields, due to its importance in many applications in public real life, such as read bank checks and formal documents which is a continuous challenge in the last years. For this motivation, the researchers created several algorithms in recognition of different human languages, but the problem of the Arabic language is still widespread. Concerning its importance in many Arab and Islamic countries, because the people of these countries speak this language, However, there is still a little work to recognize patterns of letters and digits. In this paper, a new method is proposed that used pre-trained convolutional neural networks with resnet-34 model what is known as transfer learning for recognizing digits in the arabic language that provides us a high accuracy when this type of network is applied. This work uses a famous arabic handwritten digits dataset that called MADBase that contains 60000 training and 1000 testing samples that in later steps was converted to grayscale samples for convenient handling during the training process. This proposed method recorded the highest accuracy compared to previous methods, which is 99.6%.
Co-Authors Ali Salim Rasheed Mustafa Murtdha Mustafa Murtdha Al-Saeedi Rasool Hasan Finjan