Palm print verification based deep learning

Lubab H. Albak (Northern Technical University)
Raid Rafi Omar Al-Nima (Northern Technical University)
Arwa Hamid Salih (Northern Technical University)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 19, No 3: June 2021

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.