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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Ahmed M. Alkababji
University of Mosul
Computer Science & IT

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Earprint recognition using deep learning technique Arwa H. Salih Hamdany; Aseel Thamar Ebrahem; Ahmed M. Alkababji
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16572

Abstract

Earprint has interestingly been considered for recognition systems. It refers to the shape of ear, where each person has a unique shape of earprint. It is a strong biometric pattern and it can effectively be used for authentications. In this paper, an efficient deep learning (DL) model for earprint recognition is designed. This model is named the deep earprint learning (DEL). It is a deep network that carefully designed for segmented and normalized ear patterns. IIT Delhi ear database (IITDED) version 1.0 has been exploited in this study. The best obtaining accuracy of 94% is recorded for the proposed DEL.
Real time ear recognition using deep learning Ahmed M. Alkababji; Omar H. Mohammed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.18322

Abstract

Automatic identity recognition of ear images represents an active area of interest within the biometric community. The human ear is a perfect source of data for passive person identification. Ear images can be captured from a distance and in a covert manner; this makes ear recognition technology an attractive choice for security applications and surveillance in addition to related application domains. Differing from other biometric modalities, the human ear is neither affected by expressions like faces are nor do need closer touching like fingerprints do. In this paper, a deep learning object detector called faster region based convolutional neural networks (Faster R-CNN) is used for ear detection. A convolutional neural network (CNN) is used as feature extraction. principal component analysis (PCA) and genetic algorithm are used for feature reduction and selection respectively and a fully connected artificial neural network as a matcher. The testing proved the accuracy of 97.8% percentage of success with acceptable speed and it confirmed the accuracy and robustness of the proposed system.
Half-face based recognition using principal component analysis Ahmed M. Alkababji; Sara Raed Abd
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.pp1404-1410

Abstract

Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.
Co-Authors Arwa H. Salih Hamdany Aseel Thamar Ebrahem Omar H. Mohammed Sara Raed Abd