Contactless Palmprint Recognition System Using ICANet-Based Deep Features

Feature extraction is an important task in image-based pattern recognition applications due to a large amount of different features existing in the image and its multiple application areas. Due to this necessity, a very considerable effort has been made by researchers in this direction, leading in many cases to excellent classification results. In this paper, the impact of deep learning techniques on the performance of these systems will be evaluated. For reliable assessment, a contactless palmprint-based biometric system has been developed, which is a typical pattern recognition application. In this study, a simple and lightweight deep learning architecture (ICANet) was used for the feature extraction process. The experimental results of ICANet are compared to other lightweight deep learning (PCANet and DCTNet). The results of the comparison prove the effectiveness. The experimental results of ICANet were compared to lightweight deep learning (PCANet and DCTNet) where the comparison results showed the efficiency of ICANet in terms of classification rate.