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p-Index From 2021 - 2026
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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Noor D. Al-Shakarchy
University of Kerbala
Computer Science & IT Electrical & Electronics Engineering Engineering

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Person identification based on facial biometrics in different lighting conditions Marem H. Abdulabas; Noor D. Al-Shakarchy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2086-2092

Abstract

Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
User identification based on short text using recurrent deep learning Huda Hallawi; Huda Ragheb Kadhim; Zahraa Najm Abdullah; Noor D. AL-Shakarchy; Dhamyaa A. Nasrawi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1812-1820

Abstract

Technological development is a revolutionary process by this time, it ismainly depending on electronic applications in our daily routines like(business management, banking, financial transfers, health, and other essentialtraits of life). Identification or approving identity is one of the complicatedissues within online electronic applications. Person’s writing style can beemployed as an identifying biological characteristic in order to recognize theidentity. This paper presents a new way for identifying a person in a socialmedia group using comments and based on the Deep Neural Network. Thetext samples are short text comments collected from Telegram group in Arabiclanguage (Iraqi dialect). The proposed model is able to extract the person'swriting style features in group comments based on pre-saved dataset. Theanalysis of this information and features forms the identification decision.This model exhibits a range of prolific and favorable results, the accuracy thatcomes with the proposed system reach to 92.88% (+/- 0.16%).
Co-Authors Dhamyaa A. Nasrawi Huda Hallawi Huda Ragheb Kadhim Marem H. Abdulabas Zahraa Najm Abdullah