TELKOMNIKA Telecommunication, Computing, Electronics and Control
Real time ear recognition using deep learning

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Real time ear recognition using deep learning

CNN
Ear recognition
Faster R-CNN
PCA
Real time

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.

Ahmed M. Alkababji, Omar H. Mohammed

http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 20, 2020

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