Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

Abstract

Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.

Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection

“…ey used probability calculations to identify the subblocks of the input image. More recently neural network fingerprint classification method is [16] where the proposed method is retrained over AlexNet, GoogleNet, and ResNet with an average precision of 95.55%, 92.51, and 94, 88 respectively. Matching fingerprints is the mechanism by which the similarity scores between the two fingerprints match.…”

Training Neural Networks on Top of Support Vector Machine Models for Classifying Fingerprint Images

“…Do and his colleagues [19] proposed to fine-tune recent pre-trained deep learning models such as VGG16, VGG19 [52], ResNet50 [26], Inception-v3 [56], and Xception [10] for classifying fingerprint images. Militello et al [42] showed the performance of pre-trained CNNs, including AlexNet [31], GoogLeNet [55], and ResNet [26] for the fingerprint image classification. DeepPrint network [21] learns alignment and minutiae from fingerprint images, making fixed-length fingerprint representations of only 200 bytes.…”

Convolutional Neural Networks or Vision Transformers: Who Will Win the Race for Action Recognitions in Visual Data?

“…The main reason for this success is the great diversity of the market and needs. New tasks such as medical imaging, Industry, Object Recognition [ 4 ], Autonomous Vehicle Navigation [ 5 ], Face Detection [ 6 ], Fingerprint Recognition [ 7 ], Fast Image Processing [ 8 ], and Robotic Navigation [ 9 ] have been tested at high accuracy. Furthermore, integrating artificial intelligence in image recognition is the subject of many uses.…”