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

Hussein Samma,Shahrel Azmin Suandi

doi:10.5772/intechopen.93473

Abstract

Machine learning experts expected that transfer learning will be the next research frontier. Indeed, in the era of deep learning and big data, there are many powerful pre-trained CNN models that have been deployed. Therefore, using the concept of transfer learning, these pre-trained CNN models could be re-trained to tackle a new pattern recognition problem. As such, this work is aiming to investigate the application of transferred VGG19-based CNN model to solve the problem of fingerprint liveness recognition. In particular, the transferred VGG19-based CNN model will be modified, re-trained, and finely tuned to recognize real and fake fingerprint images. Moreover, different architecture of the transferred VGG19-based CNN model has examined including shallow model, medium model, and deep model. To assess the performances of each architecture, LivDet2009 database was employed. Reported results indicated that the best recognition rate was achieved from shallow VGG19-based CNN model with 92% accuracy.

Highlights

Deep CNN models have been successfully applied for many pattern recognition problems such as human facial expression recognition [1], vehicle detection [2], and lung diseases diagnosis [3]
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
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

Summary

Introduction

Deep CNN models have been successfully applied for many pattern recognition problems such as human facial expression recognition [1], vehicle detection [2], and lung diseases diagnosis [3]. The application of CNN models for fake fingerprint recognition was investigated by Nogueira et al [4] They have studied the effectiveness of different schemes including Local Binary Patterns (LBP), SVM, VGG, and Alexnet model. These discussed models were evaluated using the dataset of liveness detection competition for the years of 2009, 2011, and 2013. Experimental results indicated that the presented ensemble model outperforms single SVM classifier They have investigated the performances of CNN as a feature extractor with ensemble model as a classifier. 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.

Architecture of pre-trained VGG19 CNN model

Transfer learning of pre-trained VGG-19 CNN model

Experimental analysis

Findings

Conclusion