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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Arwa Hamid Salih
Northern Technical University
Computer Science & IT

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Palm print verification based deep learning Lubab H. Albak; Raid Rafi Omar Al-Nima; Arwa Hamid Salih
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.16573

Abstract

In this paper, we consider a palm print characteristic which has taken wide attentions in recent studies. We focused on palm print verification problem by designing a deep network called a palm convolutional neural network (PCNN). This network is adapted to deal with two-dimensional palm print images. It is carefully designed and implemented for palm print data. Palm prints from the Hong Kong Polytechnic University Contact-free (PolyUC) 3D/2D hand images dataset are applied and evaluated. The results have reached the accuracy of 97.67%, this performance is superior and it shows that our proposed method is efficient.
Co-Authors Lubab H. Albak Raid Rafi Omar Al-Nima