**3. Transfer learning of pre-trained VGG-19 CNN model**

The basic idea of transfer learning is to employ a pre-trained network such as VGG19, then, to perform replacement for the last layer, that is, soft-max classifier.

**Figure 2.** *Transfer learning of VGG19 for fingerprint liveness detection.*

*Biometric Systems*

An incremental learning approach was given by Kho et al. [6]. The key idea is that an ensemble of SVM classifiers was constructed using boosting technique. Specifically, each base classifier in the ensemble model was trained with different subsets of the given training set. For feature extraction, three different types of handcrafted features were utilized namely LPQ, LBP, and BSIF. Experimental results indicated that the presented ensemble model outperforms single SVM classifier. In addition, they have investigated the performances of CNN as a feature extractor with ensemble model as a classifier. The outcomes show the superiority of deep CNN features against the classical hand-crafted features, that is, LPQ, LBP, and BSIF. A recent deep CNN-based approach was discussed by Fei et al. [7]. In their work VGG19, Alexnet and Mobilenet CNN models were employed. Their models were retrained with LiveDet2013 and LiveDet2015 images. The outcome indicated that the best accuracy performance was achieved from VGG19 among other CNN-based models. Nowadays, transfer learning becomes a promising technique that could be applied to utilize and reuse a powerful pre-trained CNN models to handle different pattern problems. For example, a transferred CNN models was applied for the recognition of brain tumors [8], wildfire detection [9], pneumonia diagnosis [10], seizure classification [11], remote sensing image retrieval [12], and bearing fault detection [13]. Nevertheless, the idea of transfer learning of a pre-trained CNN network is considered as a new and has not been widely studied for liveness detection. As such, this work is aiming to investigate transferring of various architectures of VGG19 CNN model to handle the problem of liveness detection. The remaining part of this chapter is organized as follows. The proposed transferred model is explained in Section 2. A series of experiments has been conducted to evaluate the effectiveness of the proposed approach is given in Section 3. A summary of the research findings

and conclusions of this study is presented in Section 4.

**2. Architecture of pre-trained VGG19 CNN model**

The basic architecture of VGG19 CNN model is given in **Figure 1**. As can be seen that VGG network contains four different types of layers namely convolution layer, max-pool layer, fully connected layer (FC), and soft-max classification layer.

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**Figure 1.** *VGG19 architecture.*

The new classification layer will be set according to the number of classes in the problem that need to be tackled. Finally, the model will be re-trained with a new training set. This idea is described in **Figure 2**.

In this study, the performance of three different architectures of VGG19 will be investigated. The transferred models include shallow, medium, and deep model as shown in **Figure 3**. For example, shallow VGG19-based CNN model contains the first and second block of VGG19. In addition, soft-max classifier has been replaced with a new classifier with two classes, that is, neurons. One neuron of soft-max is used to recognize and give probability of fake fingerprints meanwhile the second neuron is used for recognizing real fingerprints. It should be noted that the architecture of deep VGG19 CNN model contains the whole layers except the classification layer which replaced with two neurons as explained previously.
