Palm print verification based deep learning
Lubab H. Albak, Raid Rafi Omar Al-Nima, Arwa Hamid Salih · TELKOMNIKA (Telecommunication Computing Electronics and Control) · 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.