**4. Experimental analysis**

This study uses the database of LivDet2009 Database [14]. A few samples for real and fake images are shown in **Figure 4**. As described in [14], fake images were collected from a cloned fingerprint using silicon material. The total number of images used in this analysis was 1040 images for training and 2953 images for testing purposes.

The conducted analysis examined three different types of VGG architecture which are shallow, medium, and deep CNN model. Besides that, a new CNN model has been crated from scratch with the same architecture of shallow model. Each CNN model in this experiment was trained using the same training set. **Table 1** shows the outcomes for each model. As can be seen from the reported results that created CNN from scratch produced the worst performances in terms of all examined measures. This is due to lack of number of training images which usually required for building deep CNN models. On the other hand, the transferred shallow VGG19-based CNN model was able to achieve the best performances in terms of accuracy, precision, recall, and F1 score. Deep CNN model

**89**

**Table 2.**

*Transfer Learning of Pre-Trained CNN Models for Fingerprint Liveness Detection*

achieved the lowest performances among the transferred models because it lacks

Additional analysis was conducted by computing the confusion matrix for each model as reported in **Tables 2**–**5**. As shown from the results, the best true positive rate (TP) was achieved from shallow model. Specifically, the shallow model was able to correctly classify a total of 1308 cases with only 165 missing cases. In addition, shallow model reported the minimum false alarms with only 70 cases as given in **Table 3**. This is due to the benefit of transfer learning and generalization ability

Further analysis was conducted by computing the receiver operating characteristic

curve (ROC) for each studied model. ROC is shown in **Figure 5**, and the plotted curves show a very close results achieved from shallow and medium model. The worst performance was produced by a CNN model created from scratch as given in **Figure 5**. Finally, the area under the curve (AUC) measure was computed for each model as given in **Table 6**. As can be shown that AUC value resulted from the transferred models outperform the outcomes of CNN model created from a scratch. This implies that transfer learning of a pre-trained models represent a good alterative to be used instead of building a new CNN model from a scratch which required a huge

*Fingerprint examples from LivDet2009 database [14], real cases (top line), and fake cases (bottom line).*

**Approach CNN from scratch Transferred VGG19**

Actual class TP = 635 FN = 838

Accuracy (%) 69.45 92.04 86.99 81.44 Precision (%) 62.82 89.52 80.68 88.89 Recall (%) 95.67 95.27 97.36 71.96 F1 score (%) 75.84 93.27 92.31 79.54

**Shallow Medium Deep**

**Predicted class**

FP = 64 TN =1416

*DOI: http://dx.doi.org/10.5772/intechopen.93473*

as compared with deep CNN models.

training data.

**Figure 4.**

**Table 1.**

*Fake fingerprint recognition results.*

*Confusion matrix for CNN from scratch.*

for generalization as compared with shallow model.

#### *Transfer Learning of Pre-Trained CNN Models for Fingerprint Liveness Detection DOI: http://dx.doi.org/10.5772/intechopen.93473*

achieved the lowest performances among the transferred models because it lacks for generalization as compared with shallow model.

Additional analysis was conducted by computing the confusion matrix for each model as reported in **Tables 2**–**5**. As shown from the results, the best true positive rate (TP) was achieved from shallow model. Specifically, the shallow model was able to correctly classify a total of 1308 cases with only 165 missing cases. In addition, shallow model reported the minimum false alarms with only 70 cases as given in **Table 3**. This is due to the benefit of transfer learning and generalization ability as compared with deep CNN models.

Further analysis was conducted by computing the receiver operating characteristic curve (ROC) for each studied model. ROC is shown in **Figure 5**, and the plotted curves show a very close results achieved from shallow and medium model. The worst performance was produced by a CNN model created from scratch as given in **Figure 5**.

Finally, the area under the curve (AUC) measure was computed for each model as given in **Table 6**. As can be shown that AUC value resulted from the transferred models outperform the outcomes of CNN model created from a scratch. This implies that transfer learning of a pre-trained models represent a good alterative to be used instead of building a new CNN model from a scratch which required a huge training data.

#### **Figure 4.**

*Biometric Systems*

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

*Different architecture of transferred VGG19 CNN model (a) shallow (b) medium, and (c) deep.*

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

This study uses the database of LivDet2009 Database [14]. A few samples for real and fake images are shown in **Figure 4**. As described in [14], fake images were collected from a cloned fingerprint using silicon material. The total number of images used in this analysis was 1040 images for training and 2953 images for testing

The conducted analysis examined three different types of VGG architecture which are shallow, medium, and deep CNN model. Besides that, a new CNN model has been crated from scratch with the same architecture of shallow model. Each CNN model in this experiment was trained using the same training set. **Table 1** shows the outcomes for each model. As can be seen from the reported results that created CNN from scratch produced the worst performances in terms of all examined measures. This is due to lack of number of training images which usually required for building deep CNN models. On the other hand, the transferred shallow VGG19-based CNN model was able to achieve the best performances in terms of accuracy, precision, recall, and F1 score. Deep CNN model

training set. This idea is described in **Figure 2**.

**4. Experimental analysis**

which replaced with two neurons as explained previously.

**88**

purposes.

**Figure 3.**

*Fingerprint examples from LivDet2009 database [14], real cases (top line), and fake cases (bottom line).*


#### **Table 1.**

*Fake fingerprint recognition results.*


#### **Table 2.**

*Confusion matrix for CNN from scratch.*


#### **Table 3.**

*Confusion matrix for transferred VGG19 shallow.*


#### **Table 4.**

*Confusion matrix for transferred VGG19 medium.*


#### **Table 5.**

*Confusion matrix for transferred VGG19 deep.*

#### **Figure 5.**

*Results of receiver operating characteristic (ROC).*


**91**

score.

**Conflict of interest**

Authors declare no conflict of interest.

**Figure 3**.

**Figure 6.**

*model.*

**5. Conclusion**

*Transfer Learning of Pre-Trained CNN Models for Fingerprint Liveness Detection*

**Figure 6** visualizes the intermediate layers of the transferred VGG19 model for the three studied architectures, that is, shallow, medium, and deep model. As can be seen in **Figure 6** that at deep layers, the fine details of fingerprint are disappear. This is due to max-pooling operations which shrink down image size. This implies that shallow and intermediate layers produce better recognition results owing to keeping the content and details of the convolved input image as shown in

*Visualizing intermediate layers of the transferred VGG19-based models, that is, shallow, medium, and deep* 

This chapter discusses the idea of transfer learning technique of a pre-trained VGG19 model to handle the problem of liveness detection of fingerprint images. A total of three different architectures of VGG19 were examined in this chapter. These architectures include shallow, medium, and deep CNN model. The reported results confirmed the performances of the transferred VGG19 models as compared with a CNN model created from scratch. Among the transferred VGG19 models, shallow model shows the best performances in terms of accuracy, precision, recall, and F1

*DOI: http://dx.doi.org/10.5772/intechopen.93473*

#### **Table 6.**

*Fake fingerprint recognition results.*

*Transfer Learning of Pre-Trained CNN Models for Fingerprint Liveness Detection DOI: http://dx.doi.org/10.5772/intechopen.93473*

#### **Figure 6.**

*Biometric Systems*

**Table 3.**

**Table 4.**

**Table 5.**

*Confusion matrix for transferred VGG19 deep.*

*Results of receiver operating characteristic (ROC).*

*Confusion matrix for transferred VGG19 shallow.*

*Confusion matrix for transferred VGG19 medium.*

**Predicted class**

**Predicted class**

FP = 70 TN = 1410

**Predicted class**

FP = 39 TN = 1441

FP = 415 TN =1065

Medium 0.969411 Deep 0.920636

Actual class TP = 1340 FN = 133

**Approach ROC AUC** CNN from scratch 0.796899 Transferred VGG19 Shallow 0.982223

Actual class TP = 1128 FN = 345

Actual class TP = 1308 FN = 165

**90**

**Table 6.**

*Fake fingerprint recognition results.*

**Figure 5.**

*Visualizing intermediate layers of the transferred VGG19-based models, that is, shallow, medium, and deep model.*

**Figure 6** visualizes the intermediate layers of the transferred VGG19 model for the three studied architectures, that is, shallow, medium, and deep model. As can be seen in **Figure 6** that at deep layers, the fine details of fingerprint are disappear. This is due to max-pooling operations which shrink down image size. This implies that shallow and intermediate layers produce better recognition results owing to keeping the content and details of the convolved input image as shown in **Figure 3**.

#### **5. Conclusion**

This chapter discusses the idea of transfer learning technique of a pre-trained VGG19 model to handle the problem of liveness detection of fingerprint images. A total of three different architectures of VGG19 were examined in this chapter. These architectures include shallow, medium, and deep CNN model. The reported results confirmed the performances of the transferred VGG19 models as compared with a CNN model created from scratch. Among the transferred VGG19 models, shallow model shows the best performances in terms of accuracy, precision, recall, and F1 score.

#### **Conflict of interest**

Authors declare no conflict of interest.

*Biometric Systems*
