## 1 Introduction  
Biometric-based authentication has been discussed in a wide range of state-of-the-art research in the context of security applications. The increasing amount of industrial and governmental funding on this topic makes it a rapidly growing field. There are several biometric characteristics (e.g., DNA, face, and palmprint) that can be exploited in authentication systems [1]. Each one has its own pros and cons, and therefore there is no single optimal characteristic that satisfies all application requirements [2]. Consequently, based on the constraints and conditions of the operational mode, one or more biometric characteristics can be applied. There are several advantages that make the palmprint authentication more appropriate for real-world applications [3; 4; 5; 6]: (i) low-cost image acquisition devices; (ii) applicability on both low and high-resolution images; (iii) discriminative features including ridges and creases; (iv) wide region of interest; (v) unique and reliable properties; (vi) hardly affected by age; and (vii) the high user acceptance of palmprint.  
Among the existing approaches for palmprint verification, convolutional neural networks (CNNs) outperform the state-of-the-art techniques. CNNs are a class of deep learning concerned with architectures inspired by the structure and function of the brain, called artificial neural networks. Well-known CNN architectures such as Alexnet [7], VGGNet [8], and ResNet [9] have shown excellent performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) competitions [10].Although CNN-based approaches effectively represent the biometric and perceptual features from the input data [11; 12], there are some challenges in applying this concept to palmprint verification. On one hand, the number of samples is a challenging issue in current palmprint databases since the common CNN techniques need a large amount of input data for the training phase. On the other hand, CNN performance strongly depends on the deep architecture. Due to these issues, using a CNN architecture may result in overfitting in the case of small databases. It should be mentioned that data augmentation techniques can hardly reduce overfitting because of small intra-class variability among palmprint images.  
Because it is possible to use a pre-trained CNN to solve new problems faster, a paradigm called _transfer learning_ has appeared [13]. Transfer learning is useful when one wants to train a CNN on a dataset, but due to insufficient training data, it is not possible to train a full neural network on that database. Transfer learning refers to the process of taking a pre-trained CNN, replacing the fully connected layers (and potentially the last convolutional layer), and training those layers on the pertinent dataset. By freezing the weights of convolutional layers, a CNN can extract discriminative image features such as edges, and fully connected layers can take this information and use it to classify the data in a way that is pertinent to the problem. By using this concept, we can significantly address the challenges of CNN approaches, including high computational complexity in the training phase and overfitting caused by small palmprint databases. The issue that still remains a challenge in palmprint verification using CNN transfer learning is to track and detect the region of interest (ROI), which is commonly a square region in the center of the palmprint.